# Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular
Communications and Networking

Junhua Wang, Kun Zhu, _Member, IEEE_ , and Ekram Hossain, _Fellow, IEEE_ J.
Wang and K. Zhu are with the College of Computer Science and Technology,
Nanjing University of Aeronautics and Astronautics (emails: {jhua1207,
zhukun}@nuaa.edu.cn). E. Hossain is with the Department of Electrical and
Computer Engineering, University of Manitoba (email:
Ekram.Hossain@umanitoba.ca).

###### Abstract

As one of the most promising applications in future Internet of Things,
Internet of Vehicles (IoV) has been acknowledged as a fundamental technology
for developing the Intelligent Transportation Systems in smart cities. With
the emergence of the sixth generation (6G) communications technologies,
massive network infrastructures will be densely deployed and the number of
network nodes will increase exponentially, leading to extremely high energy
consumption. There has been an upsurge of interest to develop the green IoV
towards sustainable vehicular communication and networking in the 6G era.
However, as a special mobile ad-hoc network, the energy cost in an IoV system
involves the communication and computation energy in addition to the fuel
consumption and the electricity cost of moving vehicles. Moreover, the energy
harvesting technology, which is likely to be adopted widely in 6G systems,
will complicate the optimization of energy efficiency in the entire system.
Current studies focus only on part of the energy issues in IoV systems without
a comprehensive discussion of the state-of-the-art energy-efficient approaches
and the influence of the development of 6G networks on green IoV. In this
paper, we present the main considerations for green IoV from five different
scenarios, including the communication, computation, traffic, Electric
Vehicles (EVs), and energy harvesting management. The literatures relevant to
each of the scenarios are compared from the perspective of energy optimization
(e.g., with respect to resource allocation, workload scheduling, routing
design, traffic control, charging management, energy harvesting and sharing,
etc.) and the related factors affecting energy efficiency (e.g., resource
limitation, channel state, network topology, traffic condition, etc.). In
addition, we introduce the potential challenges and the emerging technologies
in 6G for developing green IoV systems. Finally, we discuss the research
trends in designing energy-efficient IoV systems.

###### Index Terms:

Green Internet of Vehicles (IoV), 6G, sustainable vehicular communications and
networking.

##  1 Introduction

As a fundamental component of the intelligent transportation systems (ITSs),
the Internet of Vehicles (IoVs) is developing quickly with the intelligent
networked vehicles, the integration of vehicular sensors, and the coordination
between vehicles, the road infrastructures and the cloud. As shown in Fig. 1,
IoV enables the interaction between vehicles and all the vehicle-related
entities via vehicle-to-everything (V2X) communication, which includes the
vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-
pedestrian (V2P), vehicle-to-network (V2N), vehicle-to-grid (V2G) and vehicle-
to-cloud (V2C) communication, etc. [1, 2, 3]. Different from a traditional
vehicular ad-hoc network (VANET) which mainly focuses on the short-range
communication between vehicles and infrastructures, IoV aims to realize wider
vehicle information services through different vehicular networking
technologies (i.e., onboard information service and heterogenous communication
networking) and intelligent technologies (i.e., cognitive computing, big data
analysis and artificial intelligence).

IoV communication system can be supported by not only the dedicated-short-
range-communication (DSRC) technology, but also the 4G/5G and future 6G
technologies with higher data rates and relatively longer transmission
distances than that of the DSRC [4]. From 1G to 6G, the communication rates
will improve from kbps to Tera bps, which will promote the development of many
new service-oriented applications. In 6G, three types of new services will be
provided, including universal mobile ultra-broadband (uMUB), ultrahigh data
density (uHDD), and ultrahigh speed low-latency communications (uHSLLC) [5].
uMUB enables the space-aerial-terrestrial-sea area communication, uHSLLC
provides ultrahigh rates and low latency, and uHDD focuses on high-density
high-reliability communication. The stringent and diversified requirements in
terms of the data rates, data density, latency, reliability and intelligence,
will bring more challenges in realizing ‘green IoV’ in the 6G era.

Energy-efficiency will be a critical issue in constructing the 6G-enabled IoV
system. First, the increasing number of connected IoV devices and the
extensive communication/computation requirements, as well as the growing
energy requirement due to the adoption of higher frequency bands in 6G, will
lead to surging energy cost in future IoV scenarios. Second, the electricity
cost of IoV infrastructures and fuel emissions of vehicles will bring growing
energy burdens to IoV system, and make it challenging to develop a sustainable
vehicular communication and networking infrastructure. Third, the stringent
requirements of quality of service (QoS) and complex intelligent decision
algorithms based big data analysis and AI in 6G-enabled IoV applications will
cause huge energy consumption and challenge the improvement of energy
efficiency.

Figure 1: Green IoV scenarios in 6G era

There has been considerable amount of researches focusing on the realization
of energy-efficient vehicular networks. For instance, to improve the energy
efficiency in V2X communication scenarios, the joint optimization of channel
allocation and communication links selection has been widely studied [6, 7,
8]. To reduce the energy consumption and improve computation utility in
vehicular edge computing scenarios, numerous works have been conducted to
optimize the offloading decisions, load distribution, and resource
allocations, etc. [9, 10, 11]. In addition, to improve fuel economy for
electric vehicles (EVs), increasing attentions have been paid on choosing the
driving routes, development the charging stations, and adjustment of driving
behaviors, etc. [12, 13, 14]. However, these works only focus on the energy-
efficiency of a single IoV scenario without a comprehensive discussion of
energy consumptions in the entire IoV system. In addition, 6G networks focus
on improving the energy-efficiency using low power communication, energy
harvesting and energy-efficient computing techniques [15]. When considering
the realization of ‘green IoV’, it is necessary to take a further step in
thinking what challenges the new 6G techniques will bring to the green IoV
world. Therefore, in this paper, we consider several critical IoV scenarios,
including the V2X communication, vehicular edge computing, traffic management,
EV energy management and energy harvesting/sharing, and introduce the energy
consumption model under each scenario. We expand the introduction by comparing
the related literatures, proposing the unsolved issues and potential
researches directions in 6G-enabled IoV systems.

We summarize the main contributions as follows:

  * •

We provide a comprehensive review of the energy-efficient IoV techniques from
the perspectives of communication networking and computation, traffic
management, energy management of EVs, energy harvesting, etc.

  * •

We introduce the most promising 6G-enabled ‘IoV’ scenarios, including the
satellite/UAV aided V2X, dynamic energy harvesting, AI-based EV charging
decisions, etc.

  * •

We discuss the current research challenges for different ‘green IoV’
scenarios, and the emerging issues related to the energy efficiency of IoV in
the context of the evolving 6G technologies.

  * •

We present the emerging networking technologies that will support the ‘green
IoV’ applications, and the potential advantages of a ‘green IoV’ system on the
promotion of future network development.

  * •

We envision important future research directions for sustainable communication
and networking in the 6G era.

The rest of the paper is organized as follows. The green IoV scenarios and the
green IoV architecture are introduced in Section 2. Then we introduce the
related researches in detail for different ‘green IoV’ scenarios, including
the V2X communication, vehicular edge computing, intelligent traffic
management, EVs and charging management, and the energy harvesting management
in Section 3, 4, 5, 6 and 7, respectively. We summarize the limitations of
existing researches and discuss the future research trend in Section 8.
Section 9 concludes the paper.

##  2 Green IoV scenarios and architecture

In the following, we introduce several ‘green IoV’ scenarios including green
IoV communication and computing, intelligent traffic management, energy
management of EVs, and energy harvesting and sharing, along with the
discussion of current research trends and challenges in each scenario.

###  2.1 Green V2X Communications

The universal connectivity and communication requirements between IoV
infrastructures and devices have been drawing significant attentions recently.
Increasing communication demands will result in huge energy consumption in
deploying IoV infrastructures and transport networks. A cloud-based vehicular
radio access network (C-VRAN) architecture can contribute to green vehicular
communications by reducing the power consumption of the supporting equipment
at base stations, minimizing the interference between remote radio heads
(RRHs), shortening the distance between RRHs and users, enabling flexible
resource sharing for virtual base stations, and utilizing data depression in
the transmission between RRHs and baseband unit (BBU) pool [16]. Besides the
architecture design, the optimization of radio resource allocation can also
improve the energy efficiency of V2X communication system [17, 18, 19]. In
addition, scheduling optimization can significantly reduce the energy
consumption in IoV applications. For example, for battery-enabled road side
units (RSUs), the energy consumption can be reduced if the RSU communicates to
a vehicle with shorter distance. Another promising method is to enable the RSU
or base station to set appropriate service scheduling modes according to the
request intensity, so as to reduce the extra energy consumption.

Besides, as a key technology of 6G network, the integrated sensing and
communication (ISAC) technology will enhance the communication performance
through sensing information [20, 21]. Due to the higher frequency bands, wider
bandwidth and denser distribution of antenna arrays used in 6G, the
integration of wireless signal sensing and communication will be possible in a
single system. In an IoV system, the V2X communication can assist a vehicle in
retrieving more traffic information outside of its sensing range, so as to
make better driving decisions and save energy consumption with suitable speed
adjustment and route planning. For example, in a vehicle platooning system,
the multihop V2V communication can enable the last vehicle to sense the
driving behaviors of the front vehicle, so as to adjust its driving speed and
direction. On the contrary, the sensing information from surrounding
environment can facilitate higher-accuracy location, imaging and environment
reconstruction, which will improve the V2X communication efficiency with
faster interruption recovery and available channel state information (CSI).
Therefore, ISAC is a promising technology to enhance the energy-efficiency in
future IoV systems.

###  2.2 Green Vehicular Edge Computing

To meet the stringent resource requirements of the computation-extensive tasks
in future IoV applications, the edge computing (EC) technology is emerging
which can run at the edge of a radio access network and provide computation
capacities for IoV devices. The energy utilization of communication and
computation can be improved by the edge/cloud computing technologies and the
resource scheduling algorithms [22, 23, 24]. However, when vehicles offload
computation-intensive tasks to other edge/cloud computing servers, the energy
consumptions of transmitting and processing the offloaded tasks increase
significantly. In addition, the edge computing servers usually have limited
communication, computation and caching capacities, hence, it is challenging to
optimize task scheduling and communication/computation resource allocation to
reduce the interferences and service delay, and minimize energy consumption in
a dynamic vehicular edge computing environment.

6G networks will use artificial intelligence (AI)-based techniques for
resource management, network control and monitoring. The concept of ‘edge AI’
has been proposed recently which pushes the network intelligence at the edge
devices, and enables the AI-based learning algorithms to run at distributed
edge devices [25]. The federated learning fits the edge computing architecture
well due to the distributed learning model, data privacy protection function
and the alleviation of communication overhead [26, 10]. However, in a
vehicular edge computing network, the mobility of vehicles becomes an issue in
keeping continuous information sharing between the edge and the cloud server.
Also, the centralized cloud server is vulnerable to security threats, which
may lead to a failure of the learning model [27].

###  2.3 Green Intelligent Traffic Management

Intelligent traffic management involves adaptive control of the traffic lights
at intersections, intelligent driving decisions (i.e., speed and distance
suggestions), and efficient route planning, etc. A well-designed traffic
management solution can facilitate the reduction of energy consumption for
both the driving vehicles and roadside infrastructures. For example, a
suitable driving speed decision will save the energy consumed in frequent
stopping/starting process [28].

In 6G-enabled IoVs, the AI-based traffic management strategies are promising
to shorten the driven delay and and improve vehicles’ energy efficiency [29,
30, 31]. For driving behavior management, deep learning-based intelligent
inter-vehicle distance control algorithm enables a vehicle to determine its
speed individually based on the online prediction of communication latency
bounds for 6G-enabled cooperative driving with hybrid communication and
channel access technologies [32]. For intelligent traffic signal control
(TSC), the reasonable TSC control method such as deep reinforcement learning
(DRL)-based TSC can improve the intersection efficiency. In [33], the authors
compared four existing methods and the corresponding vehicle speed control
strategies, including the Fixed time model (FTM), Single-objective DRL-based
TSC method (TSC-SO) which aim at minimizing overall average delay [34],
Multiple-objective DRL-based TSC method [35], and Fuel-ECO TSC (FECO-TSC)
which is designed to improve vehicle fuel economy under the synthetic scenario
and real-world scenario in the City of Toronto [36]. The authors demonstrate
the performance of the DRL-based TSC algorithm in terms of scheduling
efficiency and fuel efficiency. However, to make intelligent management
decisions, global traffic information should be collected and updated in real
time under the large-scale distributed vehicular network, leading to
increasing communication energy consumption and network operation cost. AI-
based self-learning and adaptive update algorithm will be attractive in
achieving low-complexity intelligent traffic management.

###  2.4 Energy Management of Electric Vehicles (EVs)

The vehicle itself has exhaust emissions, which brings negative effects on
human lives and the ecological environment [37, 38]. There are three main
types of vehicles including gas vehicles, electric vehicles (EVs), and hybrid
electric vehicles (HEVs). The efficient strategies for saving energy of a gas
vehicle mainly consider improving the vehicle’s mechanical performance such as
its thermal efficiency of the internal combustion engine (ICE) [33]. The
development of EVs or HEVs can reduce Green House Gas (GHG) emission by using
clean energy. Based on the data from the US Department of Energy [28], the
energy loss at the drive system of EVs can reduce to about 20% when compared
with the gasoline vehicles, where more than 60% of the energy is lost in the
form of waste heat. Meanwhile, the design of the kinetic energy recovery
system will further improve the energy utilization by recovering electrical
energy at the changing of moving states. Different energy consumption models
of EVs were presented in [39, 40], and on this basis, various optimization
models with respect to the driving paths, charging and discharging behaviors
have been designed for improving the EV’s energy efficiency [41, 42].

Another critical issue is to improve the EV charging efficiency. With the
coexistence of wired and wireless power transfer technologies, and various
charging schemes (i.e., EV-to-grid, EV-to-EV, EV-to-UAV, etc.), the
coordinated charging scheduling between multiple charging stations and EVs
becomes important. In addition, more attentions should be paid on how to
improve the power transmission efficiency, reduce the cost for establishing
power transfer system, and reduce the influences of vehicular mobility on the
charging efficiency in the wireless power transfer process.

###  2.5 Energy Harvesting Management

Besides vehicles, IoVs also involve the communication entities of road
infrastructures and other communication devices, whose energy consumptions can
be reduced by optimizing the infrastructure deployment and resource
scheduling, and by introducing the renewable energy, etc. For example, as
shown in Fig. 1, the wind or solar-powered RSUs can be installed in rural
freeway scenarios without connection to the smart grid [43, 44]. By utilizing
the big data based or AI-based power, mobile traffic and vehicle trajectory
forecasting techniques, a better matching is possible between renewable energy
utilization and workload arrivals over time and space. This can facilitate
efficient renewable energy harvesting and delivery between EVs and power edge
servers [45]. Meanwhile, since the energy requirements depend on the dynamic
traffic demands, collaborative energy management policy for various IoV
devices and infrastructures should be considered to achieve higher energy
utilization.

In addition, the radio frequency (RF) energy transfer technology can be used
to transfer energy between vehicles and RSUs [46]. For example, RSUs can sell
redundant energy to the EVs passing through RF energy transfer technology when
they have sufficient electricity. On the contrary, the RSU can purchase energy
from passing EVs when the electricity level is in a lower state. In such a
scenario, collaborative energy management between RSUs and EVs can
significantly reduce electric power consumption and improve energy utilization
[47].

###  2.6 Green IoV Architecture

Figure 2: Green SDN-based IoV architecture Figure 3: An implementation
framework

Future green IoV systems are expected to be hierarchically integrated with the
distributed edge computing components and the remote centralized computing
server. As a key enabling technique, the software-defined networking (SDN)
philosophy has been widely adopted in designing 5G-enabled vehicular networks.
Fig. 2 shows a system architecture of the green SDN-based IoV. The cloud
server acts as the centralized SDN controller. The SDN-enabled edge computing
nodes (i.e., SDN-ECs) are distributed to collect vehicular traffic data and
request information. If the SDN-ECs can meet the vehicular requests (i.e.,
computation requests) within expected deadlines, they will complete the
requests locally. Otherwise, the SDN-ECs may deliver the requests to other
SDN-ECs or the cloud center.

Fig. 3 shows an implementation framework of the green SDN-based IoV
architecture. It consists of an application layer, a control layer, and a
forwarding layer. The application layer provides different green IoV
applications, such as the green V2X communication and green edge computing.
The control layer contains the detailed implementation approaches, including
the communication/computation resource allocation, intelligent traffic
management, etc. The nodes in the data layer mainly include the vehicles and
pedestrians, etc., which may upload traffic data (i.e., position and speed)
and request information (i.e., routing and computation requests) to the
control layer. After the SDN controller makes the scheduling/routing/computing
according to the collected traffic data and requests, the decisions will be
delivered to the corresponding network nodes via controller-to-switch
messages. The scheduling decisions are stored in the flow table. For example,
in the routing scheduling, each routing rule is maintained as a flow entry,
which contains the specific request, the source and destination nodes, and the
corresponding actions.

##  3 Green IoV Communications

V2X communications play a critical role in enabling the traffic safety,
multimedia services, and intelligent driving, etc. In 6G network, Tera bps
data rate will be achieved to enable an ultra high-rate wireless bus with
ultra reliability for the future intelligent vehicles as required by high-
precision positioning and sensing applications, and various real-time
video/gaming/3D-interaction services. In addition, the grant free access and
Non-orthogonal Multiple Access (NOMA) technology will enable multiple users to
utilize non-orthogonal resources concurrently, so as to further support
massive connectivity and superior spectrum efficiency in distributed IoV
scenarios. However, since the directional beamforming is necessary for THz V2X
communications, it is challenging to utilize the advantages of beamforming in
highly dynamic traffic environment. In addition, it still lacks well-designed
energy-efficient collaboration among multi-radio access technologies (i.e.,
mmWave and THz) for interference management, channel access and resource
allocation, etc. Furthermore, it remains unexplored how to coordinate multiple
vehicles for the NOMA transmission and the current orthogonal frequency-
division scheme, and allocate resources in distributed V2X networks, so as to
achieve efficient energy utilization. Last but not the least, although 6G
supports satellite/UAV aided V2X communication with wide coverage and flexible
arial base stations, it is still challenging to design reliable and energy-
efficient communication schemes between moving UAVs and ground vehicles.
Accurate channel modeling and energy-efficient resource allocations methods
will be required in space-air-ground integrated network.

Table I summarizes the related green IoV communication techniques. In the
following, we first introduce the energy consumption model for V2X
communication, then we provide a detailed review of V2X communication
mechanisms and present how they achieve high energy-efficiency in different
vehicular communication scenarios.

TABLE I: Survey of green IoV communication techniques Publication |  Techniques for green communication |  Communication scenario
---|---|---
W. Kumar, 2014 [48] G. Sun, 2019 [49] |  Setting sleep modes |  V2I V2V
A. Shrivastava, 2018 [50] A. Hammad, 2016 [51] |  Optimizing data rates and reducing transmission overhead |  V2I
D. Saluja, 2020 [52] S. Anbalagan, 2021 [53] |  Extending communication coverage |  V2I V2X
M. Azimifar, 2016 [54] S. Vemireddy et al. [55, 56] M. Patra, 2017 [57] H. Ghazzai, 2019 [58] |  Relay-assisted forwarding |  V2I
J. Zhao, 2019 [59] |  Optimizing beamforming vectors to reduce interference |  V2V
R. Bauza, 2013 [60] D. Yoo, 2013 [61] K. Liu, 2016 [62] |  Reducing retransmissions |  V2V
A. Dua, 2015 [63] S. Sattar, 2018 [64] |  Restrictive flooding |  V2V
D. Zhang, 2013 [65] J. Chang, 2014 [66] K. Satheshkumar, 2021 [67] T. Baker, 2018 [68] |  Selecting energy-efficient relays |  V2V
A. Hammad, 2013 [69] Z. Zhou, 2018 [70] C. Zheng, 2019 [19] H. Xiao, 2020 [71] Y. Nakayama, 2020 [72] |  Optimizing resource allocation |  V2I V2V V2X
Z. Ning, 2019 [73] Y. Zhang, 2021, [74] S. Gu, 2021 [75] G. Raja, 2021 [76] T. Limbasiya, 2020 [77] C. Xu, 2018 [78] H. Wu, 2020 [79] H. Wu, 2020 [80] G. Qiao, 2020 [81] |  Edge caching strategies |  V2I V2X
P. Dong, 2016 [82] A. Dua, 2015 [63] N. Kumar, 2020 [83] |  Clustering-based routing |  V2X
S. Murugan, 2020 [84] X. Wang, 2021 [85] J. Toutouh, 2013 [86] Y. Zeng, 2013 [87] |  Optimizing routing paths |  V2X
P. Sun, 2020 [88] |  Optimizing handover operations |  V2X

###  3.1 Energy Consumption Model

In V2X communications, the majority of energy consumption comes from the IoV
infrastructures and the transmission between IoV devices. As an example, when
the BS acts as the IoV infrastructure for providing connectivity to vehicles,
we compute the communication energy consumption by focusing on the
transmission energy consumptions of vehicles and total energy consumption at
the BS. The energy consumption of the BS considers the sleep states and the
working state as follows [89, 90]:

${P_{bs}}={P_{sleep}}+{I_{bs}}\left\\{{{P_{add}}+\eta{P_{trans}}}\right\\},$
(1)

where $P_{bs}$ and $P_{trans}$ represent the total power consumption and the
maximum transmission power consumption of the BS, and
$\eta\in\left[{0,1}\right]$ represents the usage rate. $P_{sleep}$ is the
constant power consumption at the sleep state. $P_{add}$ represents the
additional constant power for computation, backhaul communication, and power
supply in active mode. $I_{bs}$ is the binary parameter which indicates the
sleep or working state of the BS. According to Eq. (1), by switching the state
of idle BSs to the sleep mode, the energy consumption of the BS can be
reduced. In addition, with suitable user association and resource allocation
solutions, the transmission energy consumption in active mode can also be
reduced.

For uplink communication between a vehicle and a BS, let us denote by $P_{v}$
and $G_{v}$ the vehicle transmission power and the channel gain for the link
to the BS, respectively. Then the maximum uplink transmission rate can be
computed as:

${R_{v}}=B\log\left({1+\frac{{{G_{v}}{P_{v}}}}{{N+I}}}\right),$ (2)

where $B$ is the transmission bandwidth, $N$ and $I$ represent the noise and
interference on the channel, respectively. The transmission energy
consumptions of the vehicle can be calculated as:

${E_{v}}=\frac{{{P_{v}}{D_{v}}}}{{{R_{v}}}},$ (3)

where $D_{v}$ is the size of transmission workload.

In the IoV, energy is consumed to support communications among vehicles, and
between vehicles and IoV infrastructures. In addition, energy is consumed to
keep the working state of the IoV infrastructures. Therefore, by adjusting the
transmission rates, choosing communication time slots, and using edge caching
nodes, the communication efficiency can be improved. For the IoV
infrastructure, it is not necessary to always keep the working state and
energy can be saved by switching the infrastructure nodes between sleep and
active modes. Furthermore, in multihop vehicular routing, energy efficiency
can be improved by controlling the routing hops, selecting suitable relays,
etc.

###  3.2 V2I Communications

V2I communications enable vehicles to upload real-time traffic data to RSUs,
and also, RSUs can broadcast emergency information to passing vehicles for
safety warnings and driving behavior suggestions. However, with the increasing
number of users and growing demands for data upload/download, the energy
consumptions of both users and RSUs are increasing rapidly. The communication
energy efficiency V2I communications can be improved through optimizing the
transmission rate, channel and time slot, reducing co-channel interference,
setting sleep mode, selecting forwarding relays and utilizing edge caching
techniques, etc.

_1) Adaptive transmission scheduling:_ When transmitting data to a user with
good channel condition, less power is required. Therefore, by estimating the
optimum data rate and optimum number of users with good channel conditions and
serving the users by multicasting the service at the optimal data rate, the
power efficiency and throughput of RSU broadcasting can be improved. The
authors in [50] consider a scheduling scenario where the RSU is installed to
provide infotainment services to the vehicles within the communication range.
Each broadcast time slot is equivalent to the coherence time during which the
channel state remains unchanged. The optimum SNR value of a channel at which
the throughput is maximized or the energy consumption per bit is minimized is
calculated. Then the optimum data rate is derived without knowing the channel
state information at the RSU. The users with SNR values higher than the
optimum SNR are selected and served with the optimum data rate. Experimental
results show that the proposed multicasting algorithm can not only improve
energy efficiency but also maximize the throughput. Similarly, the authors in
[51] propose to use a variable bit rate (VBR) air interface to reduce the
downlink RSU energy use, where the transmit power is fixed and changes of
channel path loss are compensated for by varying the transmit bit rate. The
downlink energy cost is estimated according to the vehicle’s communication
requirement, location and speed to the RSU. Then, the flow-based modes are
introduced to minimize the energy schedule generation, which are solved by the
optimal offline and greedy-based online VBR time slot schedules.

Except for the transmission rate adjustment, the adaptive transmission range
adjustment and transmission time slot allocation can also reduce RSU energy
consumption. For example, in a hybrid vehicular network architecture where
both mmWave RSUs (i.e., the RSUs that use mmWave radio access technology) and
microwave RSUs are deployed on the roadside to support vehicular
communications, the network connectivity, data rate and energy efficiency can
be improved with a well-designed association strategy between vehicular nodes
and the RSU [52]. Similarly, considering that the RSU coverage is affected by
the obstacles in city roads such as buildings and trees, the authors in [53]
propose a Memetic-based RSU (M-RSU) placement strategy to improve the network
transmission efficiency and increase the coverage area among IoV devices. The
M-RSU placement algorithm uses the received signal strength from a vehicle to
determine the coverage area of RSU, thus avoiding signal degradation and
improving the overall coverage. In addition, the transmission time slot and
packet scheduling is promising to reduce energy consumption of the RSU. The
downlink V2I energy communication costs can be reduced through dynamically
assigning communication time slot to a vehicle according to the predicted
location and velocity [69]. The packet-based scheduling and time slot-based
scheduling problems are, respectively, formulated as the single-machine job
scheduling problem with a tardiness penalty, and the Mixed-Integer Linear
Program (MILP) problem. Then, the authors design the Greedy Minimum Cost Flow
(GMCF) model to reduce the RSU energy consumption, which obtains the near-
optimal scheduling performance.

An attractive energy-saving strategy in cellular networks is to set suitable
sleep mechanisms for the BSs or access points (APs) according to the traffic
intensity [91, 92, 93]. In a vehicular network, the energy consumption of RSUs
can also be reduced through enabling the sleep modes. W. Kumar et al. [48]
propose to enable the AP to take queue-length-dependent vacations (switches of
sleep mode) to save energy with the sacrifices the quality of service (QoS) in
V2I motorway network. They modify the TDMA-based protocol as a slot-based
packet reservation multiple-access (M-PRMA) protocol, where the queue of time
slots is considered as servers and the outage of a slot represents a server on
vacation. Under this assumption, the authors study proactive and reactive
random sleep strategies. Experimental results show that the introduction of
sleep strategies at an AP can save about 80% of transmission energy during
off-peak hours.

_2) Relay-assisted scheduling and edge caching:_ The number of hops in
downlink V2I transmission may affect the energy consumption, which however, is
not the unique factor. Even though the consumed communication energy increases
with more forwarding operations, the multi-hop short-distance forwarding may
save more energy than the single-hop long-distance transmission. In
particular, with single-hop V2I communication, vehicles are served by the RSU
or BS, which can cause inefficient resource utilization for the links among
vehicles and also heavy communication burden at the RSU or BS. Therefore, by
combining the vehicle-to-vehicle (V2V) forwarding in the downlink V2I
communication, the RSU energy consumption may be reduced. M. Azimifar et al.
[54] propose an energy-efficient V2I traffic scheduling method by enabling the
RSU to dynamically forward packets through vehicles in the energy-favourable
locations. In the offline scheduling, the sequence of arriving vehicles and
their communication requirements are assumed to be known in advance, and a
scheduling algorithm is proposed to derive the lower bound on the RSU energy
to fulfill the vehicular packet communication requirements. In the online
scheduling, the authors propose two scheduling algorithms to make causal
downlink transmission decisions. One algorithm utilizes a greedy local
optimization to create schedules, and the other one uses a finite-window group
optimization (FWGO) by grouping vehicles together for joint traffic
scheduling. Simulation results show that the proposed algorithm can improve
the downlink energy requirements with V2V-assisted packet forwarding.

In a similar spirit, S. Vemireddy and R. Rout in [55] and [56] propose energy-
efficient cooperative relay scheduling strategy for transmitting data to the
target vehicles which are moving outside the RSU’s coverage. Various relay
selection algorithms are proposed, including the Minimum Cost Flow (MCF),
cluster-based approach, and the greedy algorithm, to select vehicles which are
in energy-favorable locations and use less time to reach the target vehicle.
In [55], the adopted V2V communication channel is independent of the downlink
V2I transmission channel. In [56], they further propose a forward relay
scheduler (FRS) based on auction theory to optimally assign the relay vehicles
with different time slots for minimizing the RSU energy consumption.
Experimental results show that the proposed algorithms can improve the energy-
efficiency of the RSU and the data delivery ratio to target vehicles in the
uncovered area.

In addition, with the edge caching techniques, vehicles can download contents
from the edge which facilitates the improvement of end-to-end energy
efficiency. The authors in [73] focus on the selection of edge devices (EDs)
for providing backup-resource. They put forward a Green and Sustainable
Virtual Network Embedding (GSVNE) framework to study the available number of
backup EDs and embed virtual networks onto the suitable EDs, which can serve
as many VNs as possible and provide an efficient sharing of network backup
resources. The authors in [74] focus on resource allocation optimization for
maximizing energy-efficiency in cache-based dense vehicular networks with THz
communication links. Multiple cache access points (cAPs) are deployed on the
roadside for providing vehicles with high-rate data transmission. The authors
formulate a joint subband and power allocation problem with the mean-field
approximation (MEA) method. Then each cAP is able to solve the local energy-
efficiency optimization problem by Dinkelbach method and Lagrangian dual
method. Further, a global mean-field game (MFG)-based algorithm is proposed to
associate the local optimization strategy of each cell and maximize the
energy-efficiency in the cache-based THz vehicular networks.

###  3.3 V2V Communications

The communication energy-efficiency of one-hop or multi-hop V2V transmission
has been paid wide attentions in the past few years. T. Darwish et al. [94]
give a review about green vehicular geographical routing. Several green
vehicular routing methods are introduced with objectives such as preventing
packet failure and retransmission, reducing routing overhead due to control
packets, and reducing the number of hops of packet transmission, etc. However,
this survey only considers V2I/V2V-based geographical routing without
considering 5G/6G-enabled green V2X communication, where new technologies such
as the heterogenous access network and edge caching bring both challenges and
potentials in improving communication energy efficiency. In the following, we
review some important works in the area of energy-efficiency optimization in
V2V communications.

_1) Interference management:_ To improve energy-efficiency in heterogeneous
intelligent connected vehicles (ICV) networks, J. Zhao et al. [59] study a
heterogenous access model in V2V communication, where a direct link and a
dual-hop relaying link are supported simultaneously. The dual-hop
communication is assisted by a dedicated relaying vehicle with multiple
antennas. The interferences between two data streams are tackled by jointly
designing the receive beamforming and transmit beamforming vectors at the
dedicated relaying vehicle. The authors formulate the joint optimization
problem to maximize the system energy efficiency, and decompose it into three
subproblems with only one unknown beamforming vector for each subproblem.
Finally, an iterative algorithm is used to obtain the solutions of three
subproblems. Experimental results show the energy-efficiency performance of
the proposed beamforming vector optimization algorithm.

_2) Reducing transmission/retransmission overhead:_ The most trivial solution
to disseminate data among highly dynamical vehicles is the blind flooding
which wastes large amount of energy and bandwidth due to unnecessary
transmission [63]. In a multi-hop routing process, restrictive flooding is
more energy-efficient than plain flooding under the same reliability
constraint [64]. By limiting the number of relaying nodes and using better
routing directions, the energy consumption in end-to-end routing process can
be greatly reduced.

The vehicular routing performance strongly depends on the selection of
reliable relaying nodes. With reliable multi-hop forwarders, the transmission
energy consumption from the source node to the destination node is reduced due
to the decreased retransmission operations. To identify the reliable relaying
nodes in VANETs, the authors in [60] use the periodical beacon reception rates
from the neighboring vehicles to estimate the quality of a transmission link,
and then select the more reliable transmission links to forward packets, so as
to reduce retransmission overhead and improve energy efficiency. Another
promising approach to reduce the number of retransmissions is to use the
network coding technique, which enables multiple vehicles that have common
interests in certain data to collaboratively download data with reduced number
of downloading operations [95, 62, 96]. For example, K. Liu et al. [62] adopt
the network coding techniques to reduce the number of packet transmissions in
V2I/V2V communications for improving the network throughput and system energy
efficiency. In addition, once retransmission has occurred, the communication
energy consumption can be reduced by selecting the relaying nodes with the
shortest forwarding delay to rebroadcast the data. The forwarding delay of a
vehicle can be calculated by using the vehicle’s velocity, distance and angle
from nearby vehicles [61].

_3) Adaptive transmission scheduling:_ By enabling the adaptive employment
scheme or ‘work’ mode on the relaying vehicles, they can provide varying
computation resources according to the dynamic communication requirements,
which will save the energy consumption of redundant forwarding operations. Due
to the natural characteristics of long parking time and wide distribution, the
parked vehicles have been employed as road relaying nodes to provide
communication connectivity for moving vehicles that are far apart or obscured
by obstacles. Considering that energy exhaustion may occur when parked
vehicles continuously work with no restrictions, the author in [49] propose
the energy-saving method for the parked vehicles which act as relay nodes for
providing services between driving vehicles. They classify moving vehicles
into different clusters according to their communication coverage and only
select certain parked vehicles to enter into the ‘working’ mode for energy
saving. For reducing the energy consumption of parked vehicles, the authors
use a Markov model for the energy consumption of a parked vehicle and propose
a dynamic work mode selection algorithm.

The relay selection problem has been paid wide attention in designing energy-
efficiency vehicular routing protocols [65, 67, 66, 68]. The factors related
to relay selection [65] mainly include the vehicular categories information
(i.e., public vehicle and private vehicle), the driving state of its
neighboring vehicles, the distance between the current section and the next
intersection. In [67], the relaying vehicles are selected based on vehicle
position, directional flow and message delivery time. The vehicle direction
(VD)-based authorization selection model can decrease the unnecessary message
broadcasting in vehicular routing protocol, thus reducing communication
overhead and improving energy efficiency. Similarly, in [66], the authors
leverage the information of vehicle driving direction, traffic density and the
distance between vehicles to select energy-efficient relaying vehicles. The
proposed geographical routing algorithm is shown to have higher effectiveness
compared with the ad hoc on-demand distance vector and the dynamic source
routing (DSR) algorithms. The authors in [68] propose to select the routing
paths based on the total power consumption of the relaying vehicles between
the source and destination nodes. In the proposed routing protocol (i.e.,
GreeAODV), the power consumption is calculated when the relaying node receives
a packet and transmits an incoming packet.

The joint optimization of relay selection and transmission resource allocation
is an attractive approach to reduce energy consumption in vehicular networks.
By selecting the energy-efficient relaying vehicles and allocating spectrum
and power as required by the relaying vehicles, both the communication quality
and energy-efficiency are improved. Z. Zhou et al. [70] consider to utilize a
cooperative two-hop device-to-device based vehicle-to-vehicle (D2D-V2V)
transmission to offload high volumes of vehicular data from cellular
infrastructures to vehicular networks, and formulate the joint optimization of
relay selection, spectrum allocation and power control problem from the energy
efficiency perspective. They propose a two-stage energy-efficient resource
allocation algorithm, which include an auction-matching based algorithm to
jointly optimize relay selection, spectrum allocation and power control for
maximizing energy-efficiency of two-hop D2D-V2V and cellular links in an
iterative process.

###  3.4 Hybrid V2X Communications

The above two subsections focused on the V2I and the V2V communication
scenarios, respectively. In the IoV, in addition to both the V2I and V2V
communications, more sophisticated communications scenarios such as vehicle-
to-UAV (V2U), vehicle-to-grid (V2G)111In a V2G system, plug-in electric
vehicles connect to the power grid to let electricity from to/from the
vehicles., vehicle-to-cloud (V2C), vehicle-to-pedestrian (V2P), and vehicle-
to-device (V2D) communications. In this subsection, we focus on the energy-
efficient hybrid vehicle-to-everything (V2X) communications where more than
two kinds of vehicular communication modes are involved. As shown in Fig. 4
[97], an energy-efficiency optimization approach in a vehicular communication
framework can consider the parameters related to signaling controlling, data
modulation, transmission power adjustment, routing scheduling, node’s duty-
cycle monitoring, application selection, etc. In the following, we review
various green V2X communication techniques in the literature.

Figure 4: Flowchart of 5G-based green communication algorithm for ITS

_1) Optimizing resource allocation:_ Energy-efficient resource allocation has
become a prominent direction in achieving green V2X communications. To
investigate the power-delay tradeoff in V2X-enabled cellular network, C. Zheng
et al. [19] propose an energy-efficient relay-assisted transmission scheme
based on V2X communications in uplink cellular networks. They derive the
theoretical models for both direct transmission mode and V2X-enabled
transmission mode considering circuit power and transmit power consumption. A
sequential quasiconvex optimization algorithm is first adopted for optimal
power allocation without the delay constraint based on which optimal resource
allocation is obtained to satisfy the delay constraint and minimize the energy
consumption. In [71], the authors consider a cellular network simultaneously
supporting multiple energy-harvesting-based D2D links which reuse the downlink
resources of cellular users. They formulate a joint optimization problem for
resource reuse and power allocation for D2D links in order to maximize the
energy-efficiency for cellular D2D-based V2X communication network, with the
consideration of energy harvesting constraints and quality-of-service (QoS)
constraints.

With the evolution from 5G to 6G network, more cells will be deployed to
provide wide coverage with high data rate, and the vehicle-mounted radio units
(RUs) will be promising for future mobile networks. The authors in [72]
propose an adaptive cell zooming scheme for vehicle-mounted cells, which can
adjust the size of each cell according to the current distribution of vehicle-
mounted RUs and save energy by reducing the transmission power.

_2) Edge caching strategies:_ Novel vehicular networking frameworks have been
proposed with the integration of edge caching techniques, which enable
vehicles to retrieve contents from nearby edge caching nodes instead of
directly requesting contents from the cloud servers. Both energy-efficiency
and security are improved in edge caching-enabled vehicular networks. T.
Limbasiya et al. [77] propose a vehicular cloud-based secure and energy-
efficient communication searching system, which supports secure and effective
data storage/extraction in/from the vehicular cloud, as well as the reliable
data transmission between different entities. Raja et al. [76] propose to
provide green information dissemination services by designing a software-
defined vehicular networking (SDVN) framework together with an energy-
efficient end-to-end security scheme. Compared with existing frameworks, the
proposed SDVN framework can improve the system energy efficiency by reducing
the communication and storage overhead.

To make full use of edge caching techniques for reducing communication energy
consumption, the content placement optimization has been widely researched
[79, 80, 75, 81]. The authors in [75] present a framework called cache-enabled
satellite-UAV-vehicle integrated network (CSUVIN) for implementing the popular
content distribution among multiple vehicle users (VUs), where a
geosynchronous earth orbit (GEO) satellite is regarded as a cloud server and
the UAVs are deployed as edge caching servers. Then, the authors propose a
coded caching strategy for optimizing the content placement and coded
transmission and reducing the backhaul traffic and transmission energy
consumption between GEO and UAVs. G. Qiao et al. [81] model the vehicular edge
caching optimization problem as a double time-scale Markov decision process,
where the content placement decision is made at a large time-scale, and a
joint vehicle scheduling and bandwidth allocation scheme is designed on a
small time scale. Then, the authors adopt a deep deterministic policy gradient
(DDPG) approach to solve a mixed integer linear programming problem to
minimize the content access cost.

After the contents have been placed in distributed edge/cloud nodes, the
content delivery decision is made to balance the service quality and energy
efficiency. In [78], the authors propose a green information-centric
multimedia streaming (GrIMS) framework, which enables on-demand cloud-based
processing, adaptive multi-path transmission, and cooperative in-network
caching. Considering that the 4G/LTE radio can provide long-distance
communication at the expense of high energy consumption while the WAVE/802.11p
radio supports short-distance communication with a smaller energy consumption,
they formulate a joint cost optimal problem and design different heuristic
mechanisms for GrIMS network to maximize the QoE (quality of experience) of
multimedia transmission while minimizing the network energy consumption.

_3) Clustering-based routing:_ In multi-hop vehicular routing, the total
transmission distances of all the relaying vehicles can be reduced through
optimally selecting certain vehicles as the cluster heads (CHs). With suitable
cluster management solutions, the total transmission power consumption can be
further reduced by enabling each vehicle to adjust its transmission power as
needed [82]. For the CH selection problem, the authors in [63] propose to
select the vehicles with the highest computation, storage capabilities as CHs,
and employ the vehicles with high processing capabilities to disseminate data
from the information center (i.e., CHs). Furthermore, they use a game
theoretic approach to make dissemination decisions based on the current energy
situation of the network, which can deal with the energy-consuming broadcast
storm problem. The cluster formation in [83] includes the selection of the
optimized number of control RSUs and the selection of the location of common
RSUs, where the control RSUs receive data from the traffic cloud and
disseminate to the respective vehicles through common RSUs. Considering the
heterogenous traffic data and transmission distance from the smart sensor
devices, and probabilistic delay in dynamic vehicular environment, the authors
propose the Two-Way Particle Swarm Optimization (TWPSO) algorithm to form
optimal clusters for data routing in Social Internet of Vehicles (SIoV).

_4) Optimizing routing paths:_ Due to high dynamicity, uneven distribution and
complex topology, it is challenging to design a stable and reliable routing
protocol in a vehicular network. On one hand, the unreliable routing increases
the network energy consumption due to retransmission operations in failed
forwarding. On the other hand, the frequent establishment of routing paths
requires to transmit a large number of routing requests (RREQ) and routing
reply (RREP) packets, which further consumes a considerable amount of energy.
Even though genetic algorithm has been used to search for a set of parameter
configurations (i.e., the interval of sending hello messages and topology
control message) for the classical energy-efficient OLSR (Optimized Link State
Routing) protocol [86], the routing overhead and energy consumption are still
high. To tackle the above problem, X. Wang et al. [85] consider the most
representative features of vehicles and roadways and use a nonhomogeneous
Poisson process to characterize the network connectivity. Then, a fuzzy logic-
based routing algorithm is proposed under multiple routing metrics, and show
higher routing stability and energy efficiency compared to the classical AODV
(Ad hoc On-Demand Distance Vector Routing) algorithm. For optimizing the route
selection decisions, Nash multi-agent Q-learning has been used to train
geographic forwarding and traffic data packet replication towards the optimal
direction. The objective is to improve the communication energy-efficiency of
a vehicular network under the constraints of channel congestion, buffer size,
and delay requirement [87]. The Ant Colony Optimization (ACO) and Particle
Swarm Optimization (PSO) algorithm are also applied in optimizing the routing
path between the source and destination nodes, by using the nodes’ position
and speed information [84].

In addition, the energy-efficiency of vehicular routing can be improved
through selecting the optimal relaying nodes. Except for the traditional
relaying vehicles, various network nodes can be employed as the relays. For
example, M. Patra et al. [57] propose to use mobile Femto Access Points (FAPs)
as relays between Macro Base Stations (MBSs) and vehicles, and design the sub-
channel power control optimization algorithm to handle the increased co-
channel interference between mobile vehicles. The proposed scheme can achieve
better delay and energy efficiency performance than the traditional IEEE
802.11p vehicular networks. H. Ghazzai et al. [58] propose to use unmanned
aerial vehicles (UAVs) as flying relays for delivering data from vehicles to a
mobility service center (MSC). In order to minimize the energy consumption of
UAVs during the data forwarding and flying process, the authors formulate a
routing optimization problem with the consideration of UAVs’ residual energy,
and design a meta-heuristic particle swarm optimization (PSO) algorithm to
decide the selected route and UAVs’ positions. Due to the fast movement and
frequent changes of driving direction of vehicles, energy-efficient data
dissemination becomes challenging in a realistic vehicular network
environment. Hence, it is necessary to design novel proactive handover schemes
between mobile vehicles and content providers, and enable each vehicle to
self-determine the preferred content providers in advance according to real-
time positions and routing information. Specifically, the positions of content
providers (i.e., surrounded RSUs) can be determined by taking advantage of
Doppler effects of periodically received beacon signals [88]. A well-designed
handover scheme can reduce the handover overhead, and improve the probability
of successful handover and energy efficiency in a vehicular network
environment.

###  3.5 Summary

Various green V2X communication techniques have been proposed for different
application scenarios. To save the energy consumption of IoV communication
infrastructures (i.e., RSUs and BSs), switching to the sleep mode is an
attractive approach. For uplink transmissions from vehicles to IoV
infrastructures such as RSUs and BSs, the communication energy consumption can
be reduced by selecting energy-efficient relay nodes, deploying edge caching
servers, designing highly effective proactive handover schemes, etc. For the
multiple routing problems between vehicles, the selected routing path is
expected to have higher reliability so as to reduce retransmissions, restrict
forwarding hops or choose the optimal forwarding directions so as to reduce
transmission bandwidth consumption. In addition, optimizing the resource
allocation (i.e., transmission rates and transmission time slots) is appealing
to save energy consumption under various V2X communication scenarios.

##  4 Green IoV Computation

Cloud computing and mobile edge computing (MEC) technologies enable mobile
vehicles and roadside sensors (e.g., cameras and radars) that have inadequate
computation resources to offload tasks to the powerful computing servers for
quick responses. Fig. 5 shows a general multi-layer vehicular edge computation
offloading architecture, where a vehicular user can upload tasks to an RSU
which will further offload the tasks to other edge computing servers or the
cloud server. However, while edge computing provides potential computation
resources for IoV, there are challenges in building an integrated and scalable
computing architecture with the heterogenous edge devices, balancing workload
between the cloud and edge, managing handover, and incorporating idle
resources to achieve energy-efficient computing services.

Figure 5: A hierarchical framework for vehicular edge computation offloading

In the following, we first introduce an energy consumption model for vehicular
edge computing, and then a survey on the related literatures on green
vehicular edge computing, which is summarized in Table II.

TABLE II: Survey of green IoV computation Publication |  Offloading scenario |  Offloading scheme |  Optimization problem or algorithm
---|---|---|---
H. Cho, 2020 [98] M. Liwang, 2021 [99] B. Shang, 2021 [100] Y. Zhu, 2020 [101] M. Shojafar, 2019 [102] Z. Ning, 2019 [24] Z. Zhou, 2019 [103] V. Maio, 2019 [104] H. Ke, 2020 [105] Z. Xiao, 2020 [106] |  Vehicle as resource requester |  Vehicle-to-MEC offloading |  Convex optimization Futures-based resource trading Deep learning Mixed integer convex optimization Gradient-based iterative optimization Heuristic algorithm ADMM optimization Satisfiability modulo theory method Deep reinforcement learning Game theorgy
A. Alioua, 2018 [107] M. Zhu, 2019 [108] L. Zhao, 2021 [109] Y. Liu, 2021 [110] |  Vehicle as resource requester |  Vehicle-to-UAV offloading |  Sequential game approach Deep reinforcement learning Sequential game approach Successive convex programming
R. Yadav, 2020 [111] Z. Zhou, 2019 [112] X. Wang, 2020 [113] |  Vehicle as resource provider |  User-to-vehicle offloading |  Heuristic approach Pricing-based stable matching Imitation learning based online scheduling
C. Li, 2019 [114] T. Bahreini, 2021 [115] |  Vehicle as resource provider |  Cooperative vehicular computing |  Contract theoretic approach Iterative optimization and greedy algorithms
R. Meneguette, 2020 [116] L. Pu, 2019 [117] Q. Qi, 2019 [118] |  Vehicle as resource provider |  Integrated vehicular computation offloading |  Dynamic virtual machine migration strategy Lyapunov optimization Deep reinforcement learning

###  4.1 Energy Consumption Model

If a vehicle computes a task locally, the total energy consumption of the
vehicle involves only the local computation energy. However, when a vehicle
decides to offload the computation task to an edge server, the total amount of
consumed energy includes the transmission energy from the vehicle to the edge
server and the computation energy at the edge server. Furthermore, if the
computation task is dividable, then, the vehicle and the edge server can share
the computation workload and both of them consume certain computation energy.
For simplicity, we only consider the local energy consumption model (i.e., no
offloading) and the offloading energy consumption of indivisible tasks.
Assuming that the vehicle transmits a workload with input data size $D_{v}$
(in bit) to the edge server (i.e., RSU or BS), the transmission energy
consumption can be calculated using Eq. (3). If we denote by $k_{v}$ (in CPU
cycle/bit) the computation-to-volume ratio (CVR), then the local execution
time at the vehicle is calculated as:

$t_{v}^{loc}=\frac{{{D_{v}}\cdot{k_{v}}}}{{{f_{v}}}},$ (4)

where $f_{v}$ is the local CPU cycle frequency. The local computation energy
consumption is calculated as:

$E_{v}^{loc}=p_{v}^{loc}\cdot
t_{v}^{loc}=\xi\cdot{\left({{f_{v}}}\right)^{\beta}}\cdot t_{v}^{loc},$ (5)

where $p_{v}^{loc}$ is the local power consumption which can be modeled by a
super-linear function of CPU frequency [119]. Here, both $\xi$ and $\beta$ are
constants.

If the vehicle decides to offload the entire workload to the edge server, the
execution time is calculated as [120]:

$t_{e}^{exe}=\frac{{{D_{v}}\cdot{k_{v}}}}{{f_{e}^{v}}},$ (6)

where $f_{e}^{v}$ is the CPU frequency reserved for the vehicle by the edge
server. The power consumption is expressed as [121]:

$p_{e}^{exe}=\kappa\cdot{\left({f_{e}^{v}}\right)^{\delta}},$ (7)

where $\kappa$ and $\delta$ are related system parameters of the edge server.
Then, the energy consumption of computation offloading is calculated as:

${E_{e}}={E_{v}}+E_{e}^{exe}={E_{v}}+p_{e}^{exe}\cdot t_{e}^{exe}.$ (8)

In vehicular edge computation offloading problems, the offloading decisions
(i.e., local computing or offloading, and where to offload) and workload
sharing among the edge servers affect the system energy consumption. In
addition, due to the limited resource capacities of vehicles and edge servers,
the communication resources allocated for offloading the tasks and the
computation resource allocated for processing the tasks are critical factors
that affect system energy efficiency.

###  4.2 MEC for Vehicular Users

With the rapid development of vehicular networks, future vehicles are expected
to support the intelligent navigation, self-driving, and online gaming, etc.
The onboard units (OBUs) may not have adequate computation capabilities to
process a large volume of workload with ultra-low latency requirements.
Vehicular edge computing (VEC) enables vehicles to offload computation-
intensive tasks to network edge servers for saving energy consumption. Various
edge servers can be utilized, such as the roadside computing servers that are
connected with RSUs/BSs, flying UAVs, etc.

_1) Roadside edge servers:_ By offloading tasks to roadside edge servers, the
energy consumptions at vehicles are reduced, while transmitting and processing
of the offloaded tasks to/at the edge or cloud servers consume extra energy.
The network edge servers generally have limited communication, computation and
caching capacities, and it is not always the best choice to offload all the
computation tasks to the edge servers. Meanwhile, with the deployment of more
roadside edge servers to provide communication/computation resources, the
construction cost and energy consumption will further increase and need to be
considered. Energy-efficient computation offloading strategies will be
required for optimal resource allocation and offloading decision for saving
energy in vehicular edge computing scenarios [98, 99, 101, 102, 24, 103, 104,
106].

First of all, optimization of the offloading decision is a fundamental problem
in VEC. To minimize the local energy consumption, each vehicular user chooses
either local computing or task offloading to other edge/cloud servers.
However, the edge/cloud servers may not be able to process all the workload in
a timely manner due to limited resource capacities, and the system energy-
efficiency may be affected. To optimize global energy-efficiency, Z. Zhou et
al. [103] adopt an ADMM-based distributed solution to decouple the
optimization variables (i.e., continuous offloading decisions of vehicles) and
decompose the original problem into a series of subproblems for parallel
processing. V. D. Maio et al. [104] develop a satisfiability modulo theories
(SMT) method to solve the offloading problem for validation tasks in
Blockchain-based VANETs, which aims to balance between energy-efficiency and
computation reward. Besides the computation offloading decisions, the
communication and computing resource allocations are also critical
optimization variables in VEC problems. The authors in [102] propose an
energy-efficient adaptive resource management strategy for real-time vehicular
cloud services. By exploiting the network states of the TCP/IP connections
between fog-enabled RSUs and vehicles, they design a resource management
strategy to maximize the overall communication-plus-computing energy-
efficiency, through an adaptive control of input/output traffic flows,
adaptive reconfiguration of intra-fog communication rates and virtual machine
deployments on fog nodes.

Once the computation offloading and resource allocation decisions have been
made, cooperation among multiple edge servers is important to further balance
the workload and improve computation energy efficiency. In a dynamic traffic
environment, transferring workload between edge/cloud servers will consume
more communication energy, and the intermittent connections between vehicles
and edge/cloud servers may decrease the QoS and lead to extra energy cost in
rebuilding network connections. In [24], the authors propose a heuristic
algorithm to balance the computation workload among multiple RSUs and minimize
the energy consumption due to processing the offloaded tasks under latency
constraints. In the considered system, multiple RSUs equipped with MEC servers
are connected via fault-free and (almost) delay-free wired links. They
periodically broadcast the current network status (i.e., traffic loads and
computation capabilities) to other RSUs. When a vehicle uploads the
computation tasks to the nearest RSU, it evaluates the service time of these
computation tasks based on the incoming traffic flows. If the expected service
time is longer than the tolerable delay, the computation task will be
transferred to other RSUs via multihop transmission. In the cooperative
computation offloading process, the total energy consumption includes the
energy consumption in task processing, task transmission among RSUs, and
returning the computation results. To minimize the energy consumption of all
the computation tasks while satisfying the delay constraint, a MEC-enabled
Energy-Efficient Scheduling (MEES) scheme is proposed to schedule computation
tasks among MEC servers and feed back the results in an energy-efficient
manner to reduce the downlink transmission energy consumption of RSUs.

_2) UAV as edge servers:_ The UAV-assisted vehicular edge computation
offloading is appealing for enabling intelligent transportation system (ITS)
applications. For example, by incorporating UAVs to assist emergency vehicles
in data processing, the emergency response time is reduced [107]. With the
UAVs acting as flying edge servers, the trajectory optimization together with
computation offloading and resource allocation becomes the most important
issue. M. Zhu et al. [108] use a deep deterministic policy gradient (DDPG)
method to jointly adjust the UAV’s three-dimensional flight directions,
transmission power and channel allocation actions, so as to maximize the total
throughput of UAV-to-vehicle communication and improve the system energy
efficiency. Besides being edge servers, the UAVs can also act as relays
between vehicles and other roadside edge servers. In [109], the authors
present an SDN-enabled UAV-assisted vehicular computation offloading framework
to minimize the system cost, where a UAV can act as edge server or a relay
between the vehicles and the MEC server. They formulate the offloading problem
as a multi-player computation offloading sequential game, and design the UAV-
assisted vehicular computation cost optimization (UVCO) algorithm to solve it.

One of the shortcomings of UAVs is their limited battery lifetime, which
requires a UAV to fly back periodically to the preset charging stations for
charging. However, with the development of wireless power transmission (WPT)
techniques, the UAV can be charged in wirelessly. When a UAV acts as the edge
server, it consumes computation resources to process vehicular tasks, and
meanwhile, it can be charged using WPT technique. In such a scenario, the
resource and energy management becomes complicated. A recently published work
[110] studies a UAV-assisted MEC system for a platoon of WPT-enabled vehicles,
where a platoon of vehicles equipped with radio frequency (RF) transmitters
can provide energy and offload computation tasks to the UAV. Under the
communication and computation resource constraints, the authors incorporate
the transmission power of both vehicles and the UAV, the duration of allocated
time slots for task offloading (over ground-to-air link), task computing and
result downloading (over air-to-ground link) in a joint optimization model.
Then, a successive convex approximation-based iterative programming method is
proposed to solve the joint scheduling model for improving the overall
computation capacity and QoS.

###  4.3 MEC With Vehicular Servers

With the enhanced computation capabilities of onboard units, mobile vehicles
have been employed as dynamic edge computing servers, which can provide
computation resources for nearby users (i.e., user-to-vehicle offloading), or
relieve the users’ computation workload at the BS. Meanwhile, cooperative
resource sharing among mobile vehicles (i.e., vehicle-to-vehicle offloading)
have been an attractive way to balance the workload and improve
network/computation resource utilization.

_1) User-to-vehicle offloading:_ When employing the vehicles to provide
computation resources for nearby users, large energy cost at vehicles will
occur for task processing, and hence, it is not reasonable to assume that the
vehicles will share their computation resources unconditionally in practical
application scenarios. Suitable incentive mechanisms are required to motive
vehicles as edge servers for serving nearby users. In [112], the authors
propose a contract theory-based incentive mechanism to employ vehicles as edge
servers. In the first stage, the BS designs a contract by specifying the
relationship between the required resources of users (i.e., performance) and
the received rewards of vehicles, where each contract item indicates a
distinct performance-reward association. Each vehicle chooses the desired
contract item to maximize its payoff. In the second stage, the vehicles that
have signed the contract will act as edge servers. The task assignment between
vehicles and user equipments is formulated as a two-sided matching problem,
and a pricing-based stable matching algorithm is designed to optimize the
offloading efficiency.

In different computation offloading scenarios, the energy-delay tradeoff has
always been a major research focus [122, 123]. In [111], the authors propose
to exploit the under-utilized computation resources of vehicles to share the
workload at cloudlet nodes (i.e., small-size data centers ) during peak times.
In order to jointly optimize the energy and latency in task offloading
process, a three-phase energy-efficient dynamic computation offloading and
resource allocation Scheme (ECOS) is proposed. It first evaluates the resource
demands of all the user tasks on the cloudlet nodes and detects the overloaded
nodes. Then, the optimal user task which consumes the minimal offloading cost
is selected to be offloaded onto a vehicle. Finally, they introduce a
heuristic-based joint energy and latency optimization algorithm to discover an
efficient vehicle node for processing offloaded tasks.

Besides classical game theory, contract theory, and traditional optimization
algorithms, machine learning (ML)/deep learning (DL)-based AI algorithms
including the deep reinforcement learning (DRL) [105, 9], federated learning
[124], and imitation learning [125, 113] have been adopted to optimize network
performance under dynamic wireless communication environments [100]. For
online task scheduling problems, traditional algorithms mainly use heuristic-
based searching ideas, which may have unreliable scheduling performances,
especially in highly dynamic vehicular edge computing environments. The
authors in [113] propose an imitation learning-based online task scheduling
algorithm for offloading end users’ computation tasks to cooperative vehicles.
Imitation learning enables an agent to imitate the expert’s demonstrations
which are effective solutions for the original problem. Before designing the
imitation learning-based task scheduling algorithm, the authors first
aggregate the idle resources of service provider vehicles (SPVs) within the
RSU’s coverage, and model the clustered SPVs as a queueing system during the
service time. Then, the online learning policy is obtained by following the
expert’s demonstration, which is gained by the branch-and-bound algorithm with
a few iterations. The proposed scheduling algorithm is shown to result in an
acceptable performance gap (with respect to that provided by the expert) in
terms of system energy consumption.

_2) Cooperative vehicular computing:_ The computation resource coordination
among mobile/parked vehicles with well-designed incentive mechanism can
improve the energy-efficiency of IoVs [114]. The energy savings come from the
exploitation of computational slack caused by the discrete computation
resource (i.e., CPU/GPU) settings, and the exploitation of nonlinear
relationship between these settings and the computation power consumption. For
example, when the vehicle system selects a new resource configuration (i.e.,
activating more CPU/GPU cores or increasing the voltage-frequency level) for
completing a larger workload, there may be a computational capacity slack. It
may consume more energy when the same-size task is processed by the vehicle
operating at a higher CPU frequency instead of the vehicle with lower CPU
frequency. Therefore, energy can be saved by coordinating vehicles under
different work modes for completing the offloaded task. T. Bahreini et al.
[115] propose an energy-aware resource management framework in vehicular edge
computing systems, where energy-efficiency can be improved through sharing and
coordinating computing resources among connected EVs. To determine the
participating vehicles and resource sharing duration with the unpredictable
future locations of vehicles, they design a resource selection algorithm and
an energy manager algorithm to select the vehicle state (i.e., requester or
provider), the number of workload replications, and the amount of offloaded
workload so as to minimize the computation energy consumption of all
participating vehicles. When tested under real-world dataset [126], the
cooperative vehicular computing framework shows 7% to 18% energy savings
compared to that achieved by the local computing, and 13% energy savings
compared to a baseline by offloading all the workloads to RSUs.

_3) Hybrid V2X offloading:_ The integrated computation offloading framework
enables different types of users (i.e., mobile phone users, vehicle users) to
offload tasks to various IoV network nodes (i.e., the mobile vehicles, the MEC
servers, the cloudlets, the BSs, and the cloud server, etc.) [127, 116, 117,
128, 118]. These heterogenous vehicular edge computing nodes have different
communication and computation capacities, which can provide users with
multiple choices. For example, the BS edge server can have larger
communication range and powerful computing capability. The fast-moving
vehicles may prefer to offload computation-intensive tasks to the BS edge
server. On the other hand, the roadside edge server may be more suitable for
providing low-speed users such as mobile phone users with high-rate task
uploading and relatively quick task processing. In addition, mobile vehicles
can form a vehicular cloud and provide on-demand communication and computation
resources via virtual machine (VM) migration techniques. When users offload
tasks to the integrated computing framework with the mobile vehicular cloud
and the cloudlet, the energy expenditure includes the VMs’ computation energy
in the vehicular cloud and cloudlet and the energy consumed in communications
among cloudlets, clouds and vehicles [116].

Different types of integrated vehicular computing frameworks have been
proposed together with various optimization algorithms. The offloading
framework proposed in [117] enables vehicular users to leverage the
computation resources of cooperative vehicles and the edge cloud. The authors
design a Lyapunov optimization-based online task scheduling algorithm to
minimize the energy consumption of recruited vehicles in heterogeneous
crowdsensing applications. S. Wan et al. [128] propose a 5G-enabled edge
computing (EC)-IoV system framework and present the computation offloading
scenarios between vehicles, between 5G base stations (gNBs), and between
vehicles and gNBs with the assistance of EC servers. The authors design a
joint computation task allocation and resource scheduling strategy to balance
the workload between gNBs and EC servers and improve the system energy
efficiency. Different from the above works, which assume the tasks cannot be
divided or the subtasks can be executed independently, the authors in [118]
consider multiple tasks having the data dependence. They present a vehicular
edge computation offloading framework with the BSs, cloudlets, and the
vehicular nodes acting as edge computing servers. To solve the long-term
offloading decision problem, a knowledge driven (KD) service offloading
algorithm is proposed, which utilizes a DRL model to learn from previous task
offloading and makes decision for the following offloading tasks. Due to the
learned offloading knowledge, the KD service offloading algorithm can scruple
the data dependence of following tasks, which makes it more suitable for
dynamic online offloading scenarios.

###  4.4 Summary

When powerful computation capacities of edge servers are made available to
mobile vehicles, it is important to balance the energy saving at vehicles and
the energy consumption in transmitting and processing the vehicular
computation tasks. The commonly used edge servers are the fixed edge servers
such as the roadside edge servers which can connect to vehicles via APs and
RSUs, and the mobile edge servers such as the UAVs. The energy-efficiency can
be improved through joint optimization of computation offloading decisions
(i.e., local computing or the edge/cloud computing) and resource allocations
for communication/computation. Note that, with UAVs as mobile edge servers,
the flying trajectories can be optimally determined so as to reduce energy
consumption in the flying and the hovering process. In addition, sharing of
the computation resources among vehicles can improve the resource utilization
and save computation energy of vehicles. Therefore, the main optimization
objectives involve VM migration, computation offloading decisions, and
resource allocation, etc. The mobility management is another issue, because
more energy may be consumed due to transmission interruption and unreturned
computation results in dynamic computation offloading environments.

##  5 Green Traffic Management

In order to make real-time traffic management decisions (i.e., routing
decisions and traffic light control), the global traffic information needs to
be collected and analyzed. However, it is difficult to retrieve the global
information in real time for a large-scale heterogenous and distributed
vehicular network. In addition, the traffic management strategies will be
frequently updated with the high dynamics of vehicular networks, which is
inefficient and energy-consuming. Hence, more researches need to be conducted
on the design of low-complexity intelligent algorithms which enables self-
learning and adaptive update in future IoV.

In Table III, we summarize the main approaches for green traffic management.
In the following, we first introduce the energy consumption model in different
traffic scenarios, and then, compare the existing work on green traffic signal
timing, green vehicular routing, driving behavior management, and joint
optimization of traffic management.

TABLE III: Survey of green traffic management approaches Publication |  Optimization algorithm |  Related factor |  Traffic data collection |  Traffic management approach
---|---|---|---|---
J. Zhao, 2016 [29] |  Heuristic optimization |  Vehicle types, speeds, locations and acceleration/deceleration rates, etc. |  V2I |  Traffic timing optimization
E. Shaghaghi, 2017 [30] |  Density/priority-based adaptive traffic timing |  Traffic density and queue length of vehicle clusters at intersections |  V2X |  Traffic timing optimization
L. Chen, 2017 [129] |  Cooperative intersection control |  Car type and length, acceleration habits and turning intention, etc. |  V2X |  Traffic timing optimization
X. Ge, 2014 [31] |  Heuristic optimization |  Value of time (VOT) distribution, route choice and travel demand, etc. |  |  Traffic timing optimization
D. Chandramohan, 2020 [130] |  IWD optimization |  Driving priority |  V2I |  Congestion avoidance
M. Jabbarpour, 2015 [131] |  Ant-based algorithm |  Driving directions, vehicle density and speed |  V2I |  Congestion avoidance
F. Xia, 2018 [132] |  Heuristic optimization |  Road conditions and traffic information |  |  Trip generation and traffic assignment
F. Kumbhar, 2021 [133] |  Machine learning |  Vehicle position, speed and direction |  V2X |  Compatibility time prediction of candidate routes
C. Lin, 2014 [134] E. Tirkolaee, 2020 [135] Y. Xiao, 2016 [136] |  Optimization tool |  Traveling distance, road conditions, vehicle types and loads, etc. |  |  Green traffic-based route planning
G. Mahler, 2014 [41] C. Sun, 2020 [137] J. Han, 2021 [138] S. Bae, 2019 [139] |  |  Traffic timing information |  V2X C-V2X |  Optimal velocity adjustment
A. Bakibillah, 2019 [140] |  Bayesian Gaussian learning and stochastic optimization |  Historical traffic signal data |  Traffic simulator |  Optimal velocity adjustment
Q. Lin, 2018 [141] |  Quasi-optimal analysis |  |  |  Optimal velocity adjustment
C. Sun, 2020 [137] |  Dynamic programming |  Historical traffic signal data |  V2X |  Optimal velocity adjustment
Z. Cao, 2017 [142] K. Soon, 2019 [143] C. Yu, 2018 [144] |  Optimal and heuristic algorithm |  Traffic density, lane choices, etc. |  |  Coordinated traffic light control and routing planning
C. miao, 2018 [145] L. Xiao, 2020 [146] |  Genetic algorithm Reinforcement learning |  Vehicle speed and position, traffic light information, etc. |  V2I |  Joint optimization of vehicle trajectory and speed

###  5.1 Energy Consumption Model

By considering different energy efficiency factors under different traffic
models, different fuel consumption models are adopted in the literature. One
of the most critical parameters affecting fuel consumption is the vehicle
driving speed. By using regression analysis, a speed-type fuel consumption
model has been derived for real-time optimization purpose [29]. For example,
under steady driving states, the average fuel consumption can be simplified as
a function of vehicle driving speed as follows:

$F={k_{1}}+{k_{2}}\cdot s+{k_{3}}\cdot{s^{2}},$ (9)

where $s$ is the vehicle speed, and $k_{i}$s are the model parameters.

By performing regression analysis, the fuel consumption can be expressed ini
terms of vehicle speed, vehicle load and vehicle type. In [136], the adopted
fuel consumption rates (FCR) is defined as a function of travel speed $s$ and
travel load $f$. For a vehicle type $u$ traveling on a road without slope, the
FCR can be calculated by:

$FC{R_{u}}\left({s,f}\right)={\alpha_{u}}\cdot{s^{-1}}+{\beta_{u}}\cdot{s^{2}}+{\gamma_{u}}+{\varphi_{u}}\cdot
f,$ (10)

where $\alpha_{u}$, $\beta_{u}$, $\gamma_{u}$, and $\varphi_{u}$ are the
coefficients related to the vehicle type $u$. The last term of Eq. (10) only
works for vehicles with travel loads. The total amount of CO2 emissions during
a period is evaluated by multiplying the fuel consumption rates (i.e.,
$FC{R_{u}}$) with the fuel-to-CO2 conversion rate of vehicle type $u$ and the
traveling distance of the vehicle.

In addition, with the consideration of complex road conditions, the fuel
consumption model can be simplified as a function of vehicle speed $s$, road
coefficient $\varphi$, and route length $l$ [145]:

$FC=\sum\limits_{k=1}^{{M_{r}}}{f\left({\varphi\left(k\right),s\left(k\right)}\right)\cdot\frac{{\Delta
l\left(k\right)}}{{s\left(k\right)}}},$ (11)

where $M_{r}$ is the total number of sensors located on route $r$,
$s\left(k\right)$ is the vehicle speed at sensor location $k$, and $\Delta
l\left(k\right)$ is the distance between sensor location $k$ and $k+1$.
$\varphi$ is related to the road grade ($\theta$) and rolling resistance
coefficient ($\zeta$). The road grade can be calculated by the road altitude
and road length information from the global positioning system (GPS):

$\varphi\left(k\right)=\zeta\left(k\right)\cos\theta\left(k\right)+\sin\theta\left(k\right).$
(12)

Without considering the traffic conditions, the fuel consumption of a mobile
vehicle is mainly influenced by vehicle-related factors (i.e., types, speed,
loads, etc.) and the road-related factors (i.e., length, grade, rolling
resistance coefficient, etc.). From the calculation of vehicle fuel
consumption, it can be found that by adjusting the traffic signal timing and
driving behaviors, vehicle speed can be well controlled, which can save
vehicle fuel consumption. Considering the influence of traffic conditions, the
traffic signal timing and route selection can reduce fuel consumption through
achieving congestion avoidance, reducing traveling delay, and keeping a stable
driving, etc.

###  5.2 Green Traffic Signal Timing

Compared to the highway environments, vehicles normally consume more energy
under urban traffic conditions due to frequent stopping/starting in traffic
jams or at intersections. When the vehicle speed changes quickly, extra energy
will be consumed to overcome the static inertia and sliding friction. On the
contrary, if the vehicle is driving smoothly, energy is mainly consumed to
overcome rolling friction and wind resistance [28]. Besides, the traffic
signal settings can affect vehicle fuel consumptions and fuel cost through
controlling the waiting delays at intersections. Therefore, a well-designed
traffic signal control method plays an important role in keeping stable
vehicular movements and improving energy consumption at the intersections.

Based on V2X communications, the traffic information at intersections can be
aggregated for the traffic manager/controller to make signal timing decisions.
The collected traffic data such as the vehicle types, speeds, locations and
acceleration/deceleration rates can be utilized to construct the fuel
consumption models of different vehicle types [29]. Various optimization
algorithms (i.e., iterative search, heuristic, learning based approaches) can
be designed for determining the traffic signal timing, minimizing the total
fuel consumption and traffic delay in passing through the intersection, which
are two fundamental optimization objectives in traffic signal timing system.
E. Shaghaghi et al. [30] propose a VANET-based traffic signal controlling
system (TSCS) to reduce the vehicle waiting time and the pollutant emissions
at intersections. The traffic density and prioritized movement information of
vehicles are first collected via V2V and V2I communications. Then, the
roadside traffic controllers evaluate the intersections’ traffic with the
vehicles’ queue length information, and make traffic signal timing decisions
according to the current traffic status near the intersections. Similarly,
L.-W. Chen et al. [129] propose a cooperative traffic control framework for
jointly optimizing the global throughput, the total travel time and average
CO2 emissions at multiple intersections. With collected information such as
car type and length, acceleration habits and turning intention via V2X
communications, the proposed framework enables a cooperative control of
queueing sequence and length of traffic lights for adjacent intersections.
First, the traffic flow queues are classified into three types according to
the state of traffic light, including the stationary queue at the red light,
moving queue with stationary vehicles at the beginning of a green light, and
the moving queue without stationary vehicles. Then, the adjacent intersections
are considered for evaluating the joint passing rate of the whole road
network, so as to maximize the global throughput. In addition, the authors
further consider an aging process to update the priority of road segment,
which guarantees the fairness for each road segment and realizes the green
wave driving on arterial roads. Simulation results show that the proposed
traffic control framework improves the global throughput and reduce the
average waiting time, total travel time, and average CO2 emission.

###  5.3 Green Vehicular Routing

The green vehicular routing problem (GVRP), which aims at alleviating the road
congestions, providing green wave transportation, and further minimizing the
CO2 emission and achieving overall energy saving, has attracted a significant
amount of attention.

_1) Influences of road congestion:_ Traffic congestion causes high fuel
consumption and polluted gas emission. The design of vehicle routes can help
to avoid obstacles and congestion on the road, release traffic congestion and
reduce the overall energy consumptions [130]. M. R. Jabbarpour et al. [131]
propose an Ant-based Vehicle Congestion Avoidance System (AVCAS) to reduce
fuel consumption and CO2 emissions through the optimal path selection of a
vehicle at each time interval. AVCAS includes three main phases. In the
initialization phase, the real road map exported from OpenStreetMap is
converted to a graph with a set of nodes (i.e., intersections and junctions)
and links (i.e., streets and highways). The traffic data such as the IDs and
driving directions of vehicles are sent to RSUs for predicting the travel
speed of each link. In the second phase, the shortest least congested green
paths are constructed between the source and destination based on the
collected real-time traffic information. In addition, according to the
pheromone value of each link (i.e., related to travel time, length and fuel
consumption) and instantaneous states of the vehicle (i.e., density and
velocity), the authors compute the probabilities for choosing routes at the
intersections. The pheromone value is updated timely, which enables the
dynamic selection of the optimal path with reduced traffic congestion and CO2
emissions. Simulation results show that the proposed green VTRS is able to
outperform the existing routing approaches in fuel consumption rate by 17% and
alleviate energy consumption effectively.

_2) Influences of road condition and vehicle load:_ Except traffic congestion
and traveling time, the fuel consumptions and CO2 emissions are also
influenced by the road conditions, vehicle loads, driving behaviors, and
traffic environment, etc. The urban traffic data (i.e., the road and traffic
information) can be utilized to characterize the urban mobility features,
based on which, the authors in [132] construct large-scale green urban
mobility models and design the trip generation algorithm and traffic
assignment techniques for reducing the gasoline consumptions. Similarly, the
authors in [133] utilize the vehicular mobility information (i.e., position,
speed, and direction) available through the vehicular beacon message to
estimate the compatibility time (connectivity duration) of two vehicles. Based
on an ML model and compatibility analysis, they propose a self-organizing
routing protocol, which allows vehicles to estimate or predict the
compatibility time of all the candidate routes, and select the best driving
route according to the estimation results. The analytical model and five ML
techniques including the decision tree (DT), random forest classification
(RFC), Gaussian Naive Bayes (GNB), logistic regression (LR), and support
vector machine (SVM) are evaluated on the OpenStreetMap (OSM) and SUMO
mobility trace generated dataset. Simulation results demonstrate that the
proposed scheme can achieve 2 to 3 times higher packet delivery ratio compared
to the hop count-based routing algorithms.

It is worth noting that, most of the work on logistics distribution system
have focused on the energy-related factors of GVRP such as the traveling
distance, road conditions, vehicle types and loads, etc. Researches mainly aim
to reduce logistics costs (i.e., the delivery delay, fuel consumption, etc.)
by balancing workload between vehicles and selecting the optimal driving
routes. From the perspective of supply chain management, the GVRP can be
categorized into Green-VRP, Pollution Routing Problem, and VRP in Reverse
Logistics [134]. In [135], the authors propose a robust green traffic-based
routing problem for vehicles to deliver perishable products, which aims to
optimize the routing costs (related to the traveling distance), usage costs of
vehicles, loading/unloading operation costs, delivery delay, the CO2 emissions
and fuel consumption. The problem is relaxed to a deterministic linear model
and robust optimization model, which are further validated via CPLEX solver.
Similarly, Y. Xiao et al. [136] consider to minimize the total CO2 emissions
by optimizing the customer-vehicle assignment, route selection and travel time
scheduling under time-varying traffic conditions. The constraints include the
heterogeneous vehicles (i.e., different vehicle types, CO2 emission models,
and time availabilities, etc.), time-varying traffic congestion, customer-
vehicle time windows, the vehicle capacity/range, etc. To solve the mixed
integer linear programming (MILP) problem, a hybrid algorithm of partial mixed
integer programming optimization and iterative neighborhood search (P-MIP-INS)
is proposed based on the concepts from variable neighborhood search, which is
shown to reduce emission up to additional 8% on testing data.

###  5.4 Driving Behavior Management

Speed selection according to the traffic signal timing data can assist the
vehicle to stably pass through the intersections, so as to reduce the vehicle
fuel consumption. With the assistance of V2X communications, the road-side
infrastructure (i.e., RSUs or BSs) can broadcast the traffic light cycle
information to vehicles approaching the intersection, and then, the vehicles
are able to collaboratively adjust their driving speeds and other actions, so
as to minimize the total travel delays in the road intersections and reduce
vehicles fuel consumption and CO2 emission [137, 138, 147]. However, when the
network infrastructure is not available, the historically averaged timing data
and real-time phase data can be used to predict the probability of green
traffic lights, based on which, the optimal velocity trajectory can be derived
to maximize the chance of passing through green light with improved energy
efficiency [41]. In [148], the authors propose to use the crowdsourced probe
vehicle data including the GPS coordinates and velocities of the public buses
at timestamps, to obtain the deterministic knowledge of signal phase and
timing (SPaT) information of traffic lights. In [140], a traffic simulator is
used to replicate real traffic scenario and measure driving information at the
intersections (i.e., signaling, vehicle speed, acceleration and distance to
the intersection). The collected information is trained by a Bayesian Gaussian
learning model for predicting the traveling delay and passing probability at
the intersection. A stochastic optimization algorithm is developed to make the
optimal vehicle driving planning.

Different from above works which enable vehicles to adjust driving speed
according to traffic signal data, Q. Lin et al. [141] make the fuel-optimal
speed control for vehicles traveling between arbitrary two red-signalized
intersections. In addition, since the simplification of powertrain dynamics
and fuel consumption model can cause large prediction error in making speed
planning at multiple adjacent intersections, the authors adopt more practical
fuel consumption models with the consideration of engine, transmission,
aerodynamic drag, and rolling resistance, etc. By combining the above factors,
the longitudinal behaviours and fuel characteristics in acceleration,
deceleration, and constant speed driving can be described more accurately.
Based on the fuel consumption model, the authors formulate the speed planning
problem as an open-loop optimal control problem, and adopt the Legendre
pseudospectral (LPS) technique to solve it. Through analysis, they found that
the optimal driving solution between two red-signalized intersections is
either a two-stage operation (i.e., accelerating and decelerating) or a three-
stage operation (i.e., accelerating, constant speed driving, and decelerating)
depending on the distance of the road segment and the vehicle speed limit. A
quasi-optimal analytical rule is proposed to approximate the fuel-minimized
operating strategy for the red-signalized intersections.

###  5.5 Joint Optimization of Traffic Management

As discussed in the above three subsections, the energy-efficiency of
transportation systems can be improved through various approaches, such as the
traffic signal management, driving speed control, and route planning, etc. The
joint optimization of these traffic control solutions is more promising in
realizing a green transportation system. In this subsection, we review the
recent works on joint traffic management optimization.

_1) Joint optimization of traffic signal timing and trajectories:_ The traffic
light control strategy and vehicle routing scheme can be fused and jointly
optimized to reduce urban congestion [142, 143]. In [143], the authors propose
a Pheromone-based Green Transportation System (PGTS) to reduce the GHG
emissions and urban congestions. In the first step, they define the ‘pheromone
intensity’ to characterize the traffic density of a road network. Based on the
pheromone intensity of adjacent upstream road segments, they propose an online
epsilon-support vector regression model to forecast the traffic congestion. In
the second step, a Coordinated Traffic Light Control (CTLC) strategy is
proposed to coordinate the traffic lights on the upstream and downstream of
the congested roads, so as to generate a green wave scenario and reduce GHG
emissions and fuel consumptions. In the third step, they propose a Cooperative
Green Vehicle Routing (CGVR) scheme to probabilistically distribute upstream
vehicles to the downstream road, and prevent vehicles entering into the
congested upstream road. Through simulations, they prove that the combination
of CTLC and CGVR can reduce the frequency of vehicular acceleration when
traveling between multiple intersections, which further leads to a decreasing
fuel consumption. Similarly, in [144], the authors propose an MILP model to
jointly optimize vehicle trajectories and traffic signals at an isolated
signalized intersection. First, they compute the upper and lower bounds of
vehicle arrival times at the stop bars. Second, they formulate an MILP model
to minimize the total traveling delay of vehicles passing through the
intersection by optimally deciding the phase sequences, duration of each
phase, cycle lengths, vehicle lane choices, and vehicle arrival times. Third,
they identify the vehicles that pass the stop bars in the same lane as a
platoon, and adopt an optimal control model to determine the platoon leading
vehicles’ trajectories (i.e., acceleration, speed and location) for minimizing
total fuel consumption. The proposed model enables all the vehicles to drive
through the intersection at desired speeds without stops, and achieve reduced
CO2 emissions.

_2) Joint optimization of vehicle trajectory and speed:_ In practical traffic
scenarios, the vehicle route and speed are coupled in the fuel consumption
optimization problems. C. Miao et al. [145] propose a genetic algorithm and an
adaptive real-time scheduling strategy to optimize the vehicular route and
speed for improving fuel economy. Here, the fuel consumption model is
simplified as a function of vehicle speed, road coefficient and route length.
All the related traffic data (i.e., vehicle speed and position, traffic light
information) and neighbor vehicle information are collected via V2V, V2I, and
V2C communications. On this basis, they model the vehicle macroscopic motion
planning (VMMP) problem to minimize the total fuel cost for a given source-
destination route. A genetic algorithm-based co-optimization method and an
adaptive real-time optimization algorithm are proposed to search the economic
route and speed, and update the results at certain frequency, respectively.
The proposed algorithms are shown to enhance the vehicular fuel efficiency,
for example, up to 15% fuel economy improvement when compared with the fastest
route. Similarly, in [146], the authors formulate a fuel economy optimization
problem by jointly designing the traveling route, motion and speed of
autonomous driving vehicles (AVs). The AV can receive real-time traffic
information (i.e., vehicle velocity and driving direction) from the roadside
base stations (RBS) via V2I communications. With collected traffic data, a
deep Q-network (DQN)-based algorithm is proposed, which takes the AV as a
learning agent to make actions of trajectory selection. The resulting driving
safety and fuel consumption are transformed into the agent’s reward or
penalty. By interacting with the environment, the AV can gradually attain a
collision-free and fuel-economic driving policy. In addition, the authors
improve the performance of the DQN model by developing a double deep Q-network
(DDQN) based algorithm to prevent the over-estimation of the action values.
Experimental results show a 24% fuel economy improvement compared with three
other practical driving policies.

###  5.6 Summary

In this section, we have introduced several green traffic management
approaches, including traffic signal timing, vehicle routing, driving
behaviour control, and joint optimization of these strategies. The optimal
decisions are made based on the real-time traffic information such as the
vehicle density, driving direction and velocity, which are collected at the
intersections via V2I and V2V communications. In the logistics distribution
applications, the road conditions, vehicle types, vehicle loads are also
important considerations in making green route planning since they affect the
vehicle energy consumption. Except the traditional heuristic algorithm and
optimization tool (i.e., CPLEX), the RL-based algorithms have attracted a lot
of attention in designing energy-efficient traffic management strategies. The
RL-based algorithms can deal with time-varying traffic conditions and make
real-time decisions; however, the training time and the learning efficiency of
these models are hard to guarantee in dynamic traffic scenarios.

##  6 Green Energy Management for EVs

To make vehicles more environment-friendly, the industry has made great
efforts to improve the vehicle design and engineering, such as introducing
hybrid power sources (i.e., power from both gasoline engine and the electric
motor, or from full electric motor) technology. The emerging EV technologies
diversify the ‘fuels’ for the vehicles and are regarded as effective
approaches to mitigate the environmental problems. EVs usually include hybrid
electric vehicles (HEVs) [149], plug-in hybrid electric vehicles (PHEVs), and
battery-powered electric vehicles (BEVs). The power train of HEVs combines a
gasoline engine with an electric motor and battery system, which enables HEVs
to consume less gasoline and emit less pollution than a traditional internal
combustion engine while achieving similar performance [150]. There are three
key electrical technologies for improving the energy-efficiency of HEVs,
including the thermoelectric (TE) systems for all hybrids HEVs, the integrated
starter-generator (ISG) systems for mild hybrids, and the electronic
continuously variable transmission (E-CVT) propulsion systems for full hybrids
HEVs [151]. Compared with HEVs, the PHEV has an energy storage system as well
as an internal combustion engine. The PHEVs are equipped with large battery
powers and can be charged as needed, thus further reducing the fuel
consumptions [152]. Different from HEVs and PHEVs, BEVs only use batteries for
energy resources and are considered as the future development trend of new
energy vehicles. Nowadays, EVs are mostly used to represent BEVs, if not
particularly indicated.

In the following, we first present the general EV energy consumption models
and energy consumption optimization approaches. Then, the energy-efficient EV
charging techniques, mainly the EV-to-grid and EV-to-EV charging management
optimization are compared.

###  6.1 EV Energy Consumption Model

The EVs are considered as a promising technology to reduce air pollution and
alleviate climate changes. An accurate estimation of EVs’ energy consumptions
is critical for designing green driving routes and facilitating the
development of EVs [153, 37, 38, 39]. In practice, there are various factors
that can be influential to the energy consumption of different types of
vehicles. Fig. 6 shows the potential factors impacting EVs’ energy
consumption. Except the internal factors that are associated with the
vehicle’s powertrain and efficiency parameters, the external factors include
the traffic conditions that indirectly influence the vehicle speed and
acceleration, the infrastructure-related factors such as grade and surface
roughness, ambient environment factors such as the temperature and wind speed,
and driving behaviour factors such as driving aggressiveness and driving mode
selection, etc. Table IV shows several EV energy consumption models.

Different from these studies which mainly consider the vehicle speed,
acceleration, and road conditions in calculating the fuel consumption [39, 38,
154], F. Morlock et al. [39] propose to forecast the EV energy consumption
based on characteristic speed profiles and real-time traffic data which
involves information of potential stops at traffic lights and intersections on
the trip. A continuous time-dependent speed trajectory is predicted, and based
on this, they construct a detailed EV energy consumption model which accounts
for specific energy management strategies and environmental factors. Through a
field study with Mercedes Benz experimental vehicles, the proposed algorithm
is shown to provide an accurate prediction of energy consumption for long
look-ahead horizons.

Figure 6: Factors impacting EV energy consumption [40]

Specifically, the authors in [40] propose a data-driven decomposition analysis
and estimation model for link-level EV energy consumption. Consider a vehicle
moving from point A to point B on a road segment with length $L_{link}$ and
road grade $\Theta$. Then, the total energy consumption of the vehicle is
calculated as:

${E_{total}}={E_{tractive}}+{E_{A/C}}+{E_{accessory}},$ (13)

where $E_{tractive}$ is the traction energy consumption, $E_{A/C}$ and
$E_{accessory}$ are energy consumptions at the air conditioner and other
accessories, respectively. The traction energy consumption is calculated as:

${E_{tractive}}=\Delta{E_{kinetic}}+\Delta{E_{potential}}+{E_{rolling}}+{E_{aero}}+{E_{loss}},$
(14)

where $\Delta{E_{kinetic}}$ and $\Delta{E_{potential}}$ are the changes of
vehicle kinetic energy and potential energy between two points, respectively.
$E_{rolling}$ and $E_{aero}$ are the energy consumption in overcoming the road
friction and air friction, respectively. $E_{loss}$ is the internal energy
loss (i.e., heat loss).

The energy consumption of a vehicle per unit distance (i.e., energy
consumption rate) can be derived by using a regression model, and calculated
as:

$ECR\approx\frac{\alpha}{{{L_{link}}}}+\frac{\beta}{2}\cdot
m\cdot\left({PKE+NKE}\right),$ (15)

where $\alpha$ and $\beta$ are related coefficients, $PKE$ and $NKE$ are the
cumulative positive change in kinetic energy rate and the cumulative negative
change in kinetic energy rate, respectively.

To verify the theoretical model, the authors collect the real-world traffic
data for energy consumption analysis. Firstly, two impact factors of positive
kinetic energy (PKE) and negative kinetic energy (NKE) are analytically
proposed. Next, the second-by-second vehicle state (i.e., speed, battery
current, air conditioner power, etc.) and road topology (i.e., grade)
information are collected from the test vehicle’s CAN bus. With data fusion
and time-synchronizing techniques, both the GPS data logger and filed data
from vehicle data acquisition system are used for analysis. Then, by grouping
link-based snippet including roadway type, average speed and grade, etc., the
link-level energy consumption rate can be plotted as a linear function of PKE
and NKE. In addition, the real-world congestion affects EV energy consumption
by affecting the vehicle speed. Finally, three link-level EV energy
consumption models are proposed from the perspective of different impact
factors. A linear regression fitted model and an artificial neural network
(ANN) fitted model are built to characterize these collected traffic data.
Experimental results show an improved accuracy of the proposed estimation
model over the existing models.

TABLE IV: Classification of EV energy consumption model Publication |  Vehicle type |  Related factor
---|---|---
A. Wang, 2020 [37] |  Household gasoline vehicles and EVs |  Uncertainty in extraction and fuel production, supply share, drive cycles at the same speed, vehicle composition, etc.
Z. Wu, 2018 [38] |  ICEVs and EVs |  Electricity mix, electricity generation technologies, combined heat and power scale, etc.
F. Morlock, 2020 [39] |  EVs |  Traffic factors like vehicle speed, traffic lights on the trip, etc.
X. Qi, 2018 [40] |  EVs |  Vehicle speed, battery current, air conditioner power, roadway type, average speed and grade, etc.
X. Wu, 2015 [154] |  EVs |  Vehicle velocity, acceleration and roadway grade, etc.
TABLE V: Energy efficiency optimization for different types of vehicles Publication |  Vehicle type |  Optimization algorithm |  Related factor |  Optimization variables
---|---|---|---|---
T. Guan, 2016 [155] |  EVs |  DP-based |  Travel duration, acceleration and braking, and traffic light conditions, etc. |  Velocity and target gear level over a finite horizon
X. Zeng, 2015 [42] |  HEVs |  DP-based |  Road grade and speed limit, etc. |  Electricity-to-fuel equivalence factor
T. Liu, 2017 [156] |  HEVs |  RL-based |  Power demand |  Vehicle velocity and power distribution
F. V. Cerna, 2019 [157] |  HEVs |  Linear optimization |  Charge-sustaining/charge-depleting modes, SoC of HEVs battery, and deliveries schedules |  Driving mode on fuel or electricity, and navigation strategies
G. Ma, 2018, [158] |  HEVs |  DP-based |  External vehicular dynamics (i.e., position, velocity, etc.) and internal powertrain dynamics (i.e., engine speed, SoC, etc.) |  Vehicle level coordination and powertrain level power management
C. Liu, 2020 [159] |  PHEVs |  Q-Learning and NDP |  Future trip information |  Power management
J. Liu, 2019 [160] |  PHEVs |  HDP-based |  Vehicle-speed, engine speed, motor speed, and SoC |  Power control
C.-K. Chau, 2017 [161] |  PHEVs |  DP-based |  Trip information |  Drive mode selection and path planning
Q. Zhang, 2020 [162] X. Lin, 2021 [152] |  PHEVs |  RL-based |  Power demand, vehicle speed, SoC of battery |  Route planning and engine power management

###  6.2 EV Energy Efficiency Optimization

Based on different EV energy consumption models, numerous researches have been
conducted to optimize the EV’s energy efficiency through adjusting the driving
speed and driving behaviours with the consideration of current traffic
conditions. Table V summarizes related studies on the energy-efficiency
optimization approaches for different types of vehicles.

_1) DP-based energy management:_ Dynamic programming (DP) is able to realize
the global optimization in a dynamic process, and has been regraded as the
most effective approach to solve the energy consumption problems of
EVs/HEVs/PHEVs [161, 158]. T. Guan et al. [155] proposed an EV energy-
efficiency optimization model for finding a chain of decisions of the optimal
velocity and target gear level over a finite optimization horizon, which
contains multiple discrete stages. At each stage, the optimization goal is
constrained by the maximum velocity and gear, maximum travel duration,
acceleration and braking, and traffic light conditions, etc. For solving the
problem with complex vehicle model, a stage wise forward-backward Dynamic
Programming approach is used to search for the optimal solution to reduce both
the total trip time and total energy consumption. Similarly, considering the
unknown driving route and road condition ahead, X. Zeng et al. [42] model the
road grade as a Markov chain and develop stochastic HEV fuel consumption and
battery state-of-charge (SoC) models. Then, they formulate the HEV energy
management problem as a finite-horizon Markov decision process with the
information of the vehicle location, traveling direction and terrain
information, etc. A stochastic dynamic programming-based strategy is proposed
to improve energy consumption performance by maintaining the battery SoC
within its boundaries.

Even though DP is attractive in designing the EV energy management strategies,
it suffers from heavy computation burden and requires future driving
information. Hence, other variants of DP such as the adaptive dynamic
programming (ADP) and heuristic dynamic programming (HDP) are proposed to
offer approximately optimal solutions in online energy optimization problems.
The HDP utilizes the nonlinear function fitting to estimate the optimal
solution, and adjusts the parameters of nonlinear function through RL to
gradually approach the solution of the DP. In [160], the authors propose an
HDP-based online energy management strategy to optimize the energy consumption
of PHEVs. They first adopt the back propagation neural network (BPNN) to
construct the dynamic process (i.e., including the data of vehicle-speed,
engine speed, motor speed, and SoC) of PHEVs in discrete-time domain. Then,
the HDP is used to design an online energy management controller for
minimizing the PHEV fuel consumption.

_2) Learning-based energy management:_ Learning-based energy management
strategies for HEVs/PHEVs, which can operate in different modes to improve
fuel economy, have attracted great attentions. RL is appealing to design
model-free adaptive energy management strategy by predicting the related
information of HEVs/PHEVs based on historical driving data, such as the
vehicles’ velocities, driving routes, power demands, driver behaviours,
battery lifetimes, etc. [163, 159, 162, 152]. For example, in [156], the
authors adopt the fuzzy encoding and nearest neighbor approaches to predict
the vehicle velocity, and exploit a finite-state Markov chain model to learn
the transition probabilities of power demand. Then, an RL-based energy
management strategy is introduced to determine the optimal control behaviours
and power distribution between two energy sources, so as to reduce the fuel
consumptions of HEVs. Considering the challenges of energy optimization for
PHEVs due to system complexity and physical and operational constraints, C.
Liu et al. [159] propose an optimal power management algorithm for PHEVs based
on Q-learning and neuro-dynamic programming (NDP), where the evaluated future
trip information is incorporated into NDP to make in-vehicle learning possible
and effective. Similarly, the authors in [152] also propose an energy-
efficient RL-based power management algorithm for PHEVs, which outputs the
engine working power based on the SoC of battery, engine speed, and power
demand.

Different from the above work which only focus on the PHEV energy efficiency
by optimizing the vehicle speed and powertrain level power management
algorithm, the authors in [161, 162] consider the relationship between path
planning and power management. For instance, given two driving paths with the
same length, assume that one path is flat with several traffic lights, and the
other has several upper-down slopes with no traffic lights. The time-saving
solution may select the second path with no traffic lights, while the energy-
saving solution may select the first path if the slopes on the second path
lead to more energy consumption. Therefore, the path planning and PHEV power
management should be tackled in an integrated framework. Q. Zhang et al. [162]
propose an RL-based route planning and power management algorithm for PHEVs to
reduce energy consumptions. The solution includes inner and outer control
loops. The inner loop uses the model-free RL algorithm to output the power
control policy according to driving conditions. The outer loop obtains the
minimum energy consumption path by minimizing the energy consumption of all
the candidate rounds. The effectiveness of the proposed control scheme is
demonstrated by simulations in city-wide traffic scenarios.

TABLE VI: Survey of EV charging management Publication |  Charging scheme |  Optimization approaches
---|---|---
S. Danish, 2020 [164] D. Chekired, 2018 [165] X. Hu, 2018 [166] Y. Cao, 2017 [167, 168] L. Cai, 2017 [169] |  EV-to-Grid |  Optimal selection of charging stations
Y. Zhao, 2020 [170] F. Baouche, 2014 [171] M. Zeb, 2020 [172] P. Yi, 2016 [173] K. Chaudhari, 2018 [174] Z. Sun, 2017 [175] |  EV-to-Grid |  Optimizing deployment of charging stations
H. Kikusato, 2019 [176] Y. Saputra, 2019 [177] |  EV-to-Grid |  Coordination among charging stations
M. Shurrab, 2021 [178] R. Zhang, 2019 [179] A. Koufakis, 2016 [180] |  EV-to-EV |  Optimal matching between EVs
Q. Zhang, 2020 [181] |  EV-to-EV |  Optimizing the deployment of flying UAVs

###  6.3 EV-to-Grid Charging Management

Compared to traditional gasoline-powered vehicles, EVs can run out of energy
more quickly and need to be charged in a timely manner. Fundamental issues
emerge as where to charge the EVs and how to manage the charging process. When
the EV is charged in the parking mode, selection of the charging technologies
(i.e., normal or fast charging), charging pricing, and utilization of
renewable resources are important in improving charging efficiency. When the
EV is charged in the moving mode, the key problems include the designing of EV
driving route, the deployment and selection of charging stations. In this
paper, we mainly focus on the energy-efficient charging of the EVs, and
discuss two types of charging modes, i.e., EV-to-Grid charging and EV-to-EV
charging. Table VI summarizes the related work on EV charging management.

_1) Deployment of charging stations:_ The deployment of EV charging stations,
which determines where to charge/discharge energy for the EVs, is critical to
improve the charging efficiency and energy utilization [171]. On one hand, the
locations of EV charging stations is of great importance to balance the
charging demand and power network stability, as well as improve the charging
efficiency and power network capacity [170, 175]. On the other head, the
economic cost in deploying the EV charging stations should be assessed for
realistic application scenarios [174, 172]. In [173], the authors introduce a
novel architecture, i.e., EV energy internet, to enable the EVs to transmit
and distribute energy. An EV that is charged by renewable energy sources can
discharge energy at a charging station, from which another EV may be charged.
Hence, the renewable energy is distributed in the EV network, which will
improve the renewable resource utilization and reduce greenhouse gas
emissions. Due to the great importance of charging station placement problem
in deploying an efficient EV energy internet, the authors propose two
heuristic algorithms (i.e., greedy and diffusion based) to minimize both the
number of deployed charging stations and the energy losses. The performances
of the algorithms are evaluated with realistic data set of bus systems.
Experimental results show that with the greedy algorithm, it requires a fewer
number of charging stations to cover the whole bus system, and the diffusion-
based heuristic leads to less energy transmission losses.

_2) Selection of charging stations:_ After the charging stations have been
deployed in the EV network, the charging station selection problem should be
solved considering the security and privacy of EV users, availability of the
reserved time slots, QoS, EV user comfort, and energy efficiency, etc. [164].
Based on V2X communications, EVs can receive information from nearby charging
stations and make energy-efficient charging station selections [168]. For
example, Y. Cao et al. [167] propose a communication framework for battery-
switch based EV charging. Each charging station is connected to all of the
RSUs, and broadcast the condition information (i.e., availability of batteries
for switching) to RSUs via V2I communications. On this basis, the EVs can
choose the suitable charging stations for battery switch services. The
charging station selection logic considers the time for the EV to travel
towards the selected charging station and travel from the selected charging
station to the EV’s trip destination, as well as the time to spend at the
selected charging station. Similarly, L. Cai et al. [169] propose a V2X
communication architecture to facilitate energy-efficient EV charging services
in both the charging station scenario and the distributed home charging
scenario. With hybrid vehicle communication networks, the EVs can find
suitable charging stations and make the best choices according to the
potential travel cost, the expected waiting delay and charging cost, etc.
Specifically, the real-time communications between the power grid and the EVs
enable reliable and energy-efficient charging of a large number of EVs at the
same time.

_3) Coordination among charging stations:_ For EV networks, prediction of
energy demands is fundamental for coordinating the charging services among
charging stations. An SDN-enabled EV charging network architecture can provide
a global view of the EV energy demands, the deployment and capacities of
charging stations [182, 166]. In [165], the authors present a hierarchical
software-defined-network (SDN)-based wireless vehicular fog architecture
called a hierarchical SDN for vehicular fog (HSVF). The data plane includes
data-transmission devices such as RSUs, BSs, and vehicles. The decentralized
control plane includes SDN-Fs that are deployed in fog data centers. The SDN
control (SDN-C) plane is responsible for constructing the global network
connectivity by aggregating information from SDN-Fs and making scheduling
decisions (i.e., EV charging and discharging demands) based on the trajectory
prediction module, which can mitigate the frequent handover problems with the
RSUs and vehicles. Similarly, Y. M. Saputra et al. [177] introduce a
centralized communication model for the charging station provider (CSP) to
collect information of all the charging stations. A DL-based energy demand
learning (EDL) method is developed for the CSP to predict energy demands of
the charging stations. Then, to reduce communication overhead and protect data
privacy, they further propose to predict the energy demands for EV networks
with federated learning, which enables the charging stations to share
information without exposing real data sets. The proposed federated energy
demand learning (FEDL) approach can improve the prediction accuracy of energy
demands up to 24% and reduce communication overhead by 83% compared with other
baseline ML algorithms.

A cooperative charge/discharge management system can be designed to coordinate
energy distribution among multiple charging stations, and even different types
of charging stations. Specifically, to reduce CO2 emissions and solve the
energy self-sufficiency issue, the concept of net-zero energy houses (ZEHs) is
proposed, which aims to realize an annual net energy consumption of zero or
less [183]. As an important component of ZEHs, the home energy management
system (HEMS) maintains the residential convenience by monitoring and
controlling the home appliances, energy harvesting, etc. [184]. In HEMS, the
EVs can be used to minimize the residential operation cost with
charge/discharge management schemes. To coordinate the HEMS and grid energy
management system (GEMS) for reducing residential operation cost, the authors
in [176] propose an EV charge/discharge management framework for effective
energy utilization. Based on the voltage constraint information in the power
distribution system obtained from GEMS, HEMS determines an EV charging plan to
improve the energy-efficiency and minimize operating costs without interfering
with EV driving.

###  6.4 EV-to-EV Charging Management

Considering that EVs must be continuously charged due to the low capacity of
on-board batteries, and the desire to reduce energy costs, the charging
problem of EVs becomes very challenging. Recent works have proposed to
investigate more flexible EV-to-EV charging strategies, which can offload the
EV charging loads from the electric power systems. With one EV acting as the
energy consumer and another EV acting as the energy provider, cooperative
energy transfer and sharing are enabled in EV networks. One of the most
promising technologies to enable EV-to-EV charging is the wireless power
transfer (WPT). Different from traditional wired charging technology, the WPT
allows vehicles to be charged while they are moving, which fills the gap of
short driving ranges and long charging time of the EVs, and further improves
EVs’ market penetration ratio and the energy-efficiency of EV network [185,
186]. Specifically, to improve the customer satisfaction and energy
utilization compared the scenario where only EV-to-Grid charging is enabled,
the authors in [180] propose an energy scheduling scheme with the EV-to-Grid
(V2G) and EV-to-EV energy transfer. They formulate the energy transfer
problems as mixed integer programs and solve them offline and optimally in
order to improve customer satisfaction and energy utilization. In addition,
the authors propose to use a centralized charging station to schedule the
offline and online EV charging algorithms, for reducing the charging cost and
improving the energy utilization through EV-to-EV energy transfer. They also
recommend to introduce a virtual EV to store renewable energy and transfer it
to the EVs that is about to arrive at the charging station [187].

In addition, to make EV-to-EV charging scheduling, a matching based strategy
is suitable for selecting EV-to-EV pairs and managing energy transfer among
EVs, and further optimizing the users satisfaction and energy-efficiency [179,
178]. R. Zhang et al. [179] propose a flexible EV charging management protocol
with EV-to-EV matching mechanism. The EVs as energy consumers and the EVs as
energy providers can send their real-time personal information and energy
transaction requests to the control center via mobile applications or on-board
apps. Then, the control center makes charging/discharging decisions for the
involved EVs based on an EV matching algorithm. To achieve an optimal
matching, a weighted bipartite graph is constructed to model the
charging/discharging cooperation among the EVs, based on which, the maximum
weight EV-to-EV matching is obtained. Simulation results verify the efficiency
of the proposed EV-to-EV matching based charging strategy in improving network
social welfare and reducing EV energy consumption. Similarly, the authors in
[178] propose a two-layer matching approach for the EV-to-EV energy sharing
problem. A realistic optimization model with user satisfaction, system energy-
efficiency, social welfare, and the cost and profit of the EV users is
considered. On this basis, they first leverage the Gale-Shapley game [188] to