Preface

The transportation sector is progressing rapidly. The mechanized transportation vehicles that we began using 200 years ago have made our lives easier, as have their counterparts produced for different functions and purposes.

Vehicles are transportation fixtures with complex engineering features. Vehicles have their own production methods, usage areas, types, and sizes. Their one commonality is their utility for getting drivers and users from one point to another.

One of the important aspects of vehicle engineering is movement direction. How the functions of the vehicle will correspond to the commands from the driver or user is a branch of science known as "vehicle dynamics."

The first issue in vehicle dynamics is how to calculate the forces on the vehicle while it is in motion and how to calculate the energy required for this motion to occur. Although vehicle dynamism is calculated in different ways for different transportation types (air, land, sea, and space), it serves two main purposes. First, how the driver's/user's commands/requests will be executed by the vehicle and second, how safe and comfortable driving should continue while these commands are being carried out.

Vehicles are not just about their engines (internal combustion, electric, etc.), which supply energy. Besides examining the complex mechanical processes in engines, vehicle dynamics involves studying how the motion energy from a vehicle's engine enables the movement of the vehicle.

There are several important parts in vehicle dynamics that must be identified, calculated, and manufactured accordingly. These parts play an essential role in the movement of the vehicle. Brakes, suspensions, wheels, wings, aerodynamic structure, steering, driver support systems, and many more parts form the basis of vehicle dynamics.

This book highlights the importance of vehicle dynamics, with a focus on autonomous vehicles, underscoring their importance as vehicles of the future. Chapters address such topics as vehicle dynamics and modeling, testing, and modeling in different vehicles.

I wish to express my thanks to the contributing authors for their strong efforts and participation in this project.

#### **Hüseyin Turan Arat**

Associate Professor, Faculty of Engineering and Architecture, Department of Mechanical Engineering, Sinop University, Sinop, Turkey

Section 1 Introduction

### **Chapter 1**

## Introductory Chapter: The Dynamics of Vehicles – Basics, Simulation and Autonomous Systems

*Hüseyin Turan Arat*

#### **1. Introduction**

Today, developments in the transportation sector are progressing in a huge promoting way. Our mechanized transportation vehicles that we started to use 200 years ago has made our lives easier with their counterparts produced for different functions and purposes.

Vehicles are transportation fixtures with complex engineering features. All of them have their own production types, usage areas, types, and sizes. Totally, all of them are in the production area and during usage are made to reach the driver or users from one point to another.

This is undoubtedly that one of the important arguments of vehicle engineering is movement direction. How the functions will correspond to the commands that are requested from the tool should be done as classified in a teaching department. The name of this branch of science is "vehicle dynamics."

When it comes to vehicle dynamics, the first thing that comes to mind is that it is the issue of how to calculate the forces on the vehicle while it is in motion and how to calculate the energy required for this motion to occur. Although vehicle dynamism is calculated in different ways for different transportation types (air, land, sea, and space) it serves two main purposes:

Consequently, much of the studies that are carried out for many years relating to conceptual and mathematical equations are associated with vehicle dynamics and have been created for all kinds of vehicles and started to be used on real vehicles. All this mixture of equations of motion realized by the basic requirements of physics and the mathematics of motion mechanisms are available to the driver.

Vehicles are not just about engines (internal combustion, electric, etc.) which are energy sources. Besides working with complex mechanical processes in engines, the main purpose is about how the motion energy from the engines will provide the movement of the vehicle.

According to reachable open literature, there are important parts in vehicle dynamics that must be found, calculated, and manufactured accordingly. These parts play a very essential and key role in the movement of the vehicle. Brakes, suspensions, wheels, wings, aerodynamic structure, steering, driver support systems, and many more parts formed the basis of vehicle dynamics.

### **2. Discussion**

For the last 20 years, production strategies and product diversity, especially in vehicles used in land transportation, are constantly developing and renewed in line with the wishes of its users. Undoubtedly, technological development, human factor, environmental factor, and necessity factor have an important place in this renewal.

The safety and driver-assisted security processes of the vehicles play an active role in the production of electromobility and semiartificial intelligence vehicles. Although the transition to the fifth phase (fully autonomous vehicles: fully automatic vehicles without any interference from the driver) is ongoing, it still has some more time to fully develop. However, fourth phase is active in most vehicles.

The vehicle dynamics concerned details and the development process is roughly illustrated in **Figure 1**.

Nearly 90% of traffic accidents in the world are due to driver's fault [1]. Therefore, to support/assist the drivers vehicles are integrated with driver-assistance devices and systems. These systems are developed in such a way that they can perform instant analysis and react with a control unit. The phenomenon of "assistant" that started with ABS and ESC has taken a highly developed form with new

**Figure 1.** *An illustrated figure on vehicle dynamics (photos taken from Google Images).*

#### *Introductory Chapter: The Dynamics of Vehicles – Basics, Simulation and Autonomous Systems DOI: http://dx.doi.org/10.5772/intechopen.110835*

#### **Figure 3.**

*The system components and sensor design of an autonomous vehicle [5].*

technologies. In an example as is illustrated in **Figure 2** shows the driving assistance systems of the Audi A7 concept.

Most of these support systems are produced to increase the interaction of the driver with the vehicle and to achieve a safe and comfortable riding more easily. Hundreds of hours of simulation and practical experiments are carried out under these productions. After these trials, the optimized systems become systems that are manufactured and coupled to the vehicle. In engineering applications, before

creating a product, it is inevitable to create an analysis with simulation and programming. The math behind this procedure is done with basic vehicle dynamics math. Mathematical calculations of different organs and major case studies can be found in Refs. [3, 4].

More autonomous driving and driverless operations of such vehicles starting to run in the coming years. Certain companies (Google, Tesla, etc.) continue to work and try on autonomous vehicles.

The system components and sensor design of an autonomous vehicle is shown in **Figure 3** [5].

#### **3. Conclusion**

As a general result, this study's aim is to create a special book to explain the importance of vehicle dynamics, which has an important place in the automotive and vehicle world and to direct more attention to autonomous vehicles.

The book chapters were created by experts in their fields and blended with the aim of both vehicle dynamics and modeling, testing, and modeling in different vehicles, and presenting the main information.

This book presents to the readers crucial phenomenon about the autonomous vehicles of the future and how important the vehicle dynamics is.

### **Conflict of interest**

"The author declares no conflict of interest."

#### **Author details**

Hüseyin Turan Arat Faculty of Engineering and Architecture, Department of Mechanical Engineering, Sinop University, Sinop, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Introductory Chapter: The Dynamics of Vehicles – Basics, Simulation and Autonomous Systems DOI: http://dx.doi.org/10.5772/intechopen.110835*

#### **References**

[1] Available from: https://www.cbmclaw. com/what-percentage-of-car-accidentsare-caused-by-human-error/ [Internet]. 2023

[2] Audi RS7 Driver Assistant Systems [Internet]. 2023. Available from: https:// www.audi-mediacenter.com/en/photos

[3] Guiggiani M. The Science of Vehicle Dynamics: Handling, Braking, and Ride of Road and Race Cars. Dordrecht: Springer; 2013. p. 356. DOI: 10.1007/978-94-017-8533-4

[4] Rajamani R. Vehicle Dynamics and Control. New York, NY: Springer; 2006. DOI: 10.1007/978-1-4614-1433-9

[5] Arat HT, Baltacioğlu MK, Conker C. Electric vehicles and future of transport sector. In: Muhommed A, editor. Handbook of Energy Transitions. Taylor Francis CRC Press; 2022. pp. 151-165. DOI: 10.1201/9781003315353-10

Section 2 Modelling

#### **Chapter 2**

## Modelling and Dynamic Analysis of a Vehicle

*Burak Can Çiçek*

#### **Abstract**

In this topic, modelling of vehicles, dynamic analysis and verification of both vehicle model and dynamic analysis will be elaborated. Vehicle modelling can be performed by either using simple discrete models, finite element models or rigid bodies that are connected with elastic elements. Element types of each modelling method and in which circumstances can be used will be explained. Advantages and disadvantages will be encompassed. After that, vehicle model will be evaluated in terms of dynamic behaviours such as mode shapes and natural frequencies. Moreover, damping parameter selection methodology will be elaborated. Consequently, model verification methodologies will be handled such as modal tests and dynamic load tests. How the model can be updated according to the tests will be briefly explained.

**Keywords:** finite element model, modal analysis, dynamic analysis, verification test, vehicle

#### **1. Introduction**

In this chapter, modelling of vehicle, methods of dynamic analysis of a vehicle and verification tests of the methods will be explained. Moreover, industrial experience in modelling, analysis and verification will be shared.

The vehicle market evolution requires reduction of time to market. Moreover, it is inevitable to decrease the time of concept to mass production. Furthermore, new vehicle models are revealed every couple of years in order to the survival of vehicle companies. It is merely possible by performing most of the verifications by analyses. Building models and relying on analyses for verification requires experience. Therefore, modelling methods, verification of models, analysis methods, verification of analysis and test methods are elaborated to share theoretical and industrial experience with the readers.

#### **2. Modelling of a vehicle**

In order to produce a vehicle, design phase is required to be completed. After the vehicle design verification, production phase starts. Some modifications will be needed to be performed before the final product such as design improvements,

customer requests, etc. Performing verification tests after every modification will cause overbudget. Moreover, the production of each prototype will cause overschedule. Therefore, other verification methods will be needed such as verification of models and analyses. Models should be created before analyses are performed. There are two types of models such as analytical models and finite element models.

#### **2.1 Analytical models**

In analytical methods, there are two methods such as creating simple discrete models and creating flexible models. After creating these two mathematical models, analytical solutions can be calculated.

Advantages of analytical methods can be summarized as follows:

Disadvantages of analytical methods can be summarized as follows:

#### *2.1.1 Simple discrete model*

Simple discrete models can be used at the beginning of the design phase. Simple discrete models are particularly utilized for a quick and approximate solution since they have tens of degrees of freedom. Rigid body and flexible joint assumptions can be utilized in order to obtain analytical model. Chassis and the upper structure of the vehicle can be modelled as a single rigid body that has mass and inertia or a set of point masses that have inertia. In this approach, suspension systems and tires can be modelled as point mass and inertia with their stiffness and damping. Dynamic behaviour of a vehicle can be calculated by using Lagrange's equation shown in Eqs. (1) and (2) [1].

$$\frac{d}{dt}\left(\frac{\partial \mathfrak{E}}{\partial \dot{q}\_r}\right) - \frac{\partial \mathfrak{E}}{\partial q\_r} = Q\_r^\* \text{ } \mathbf{r} = \mathbf{0}, \dots, \mathbf{n} \tag{1}$$

$$
\mathfrak{e} = E\_k - E\_p \tag{2}
$$

*Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

where,

£ is Lagrangian

qr, r=1, … ,n are generalized coordinates

Qr \* , r=1, … ,n are generalized forces representing all non-conservative forces

Ek is Kinetic Energy

Ep is Potential Energy

There are three types of simple discrete vehicle modes such as quarter vehicle model, half vehicle model and full vehicle model. Quarter simple discrete vehicle model, which is one-fourth of the whole vehicle, is shown in **Figure 1**. Quarter simple discrete vehicle model is the simplest version of a vehicle model. It contains only one tire, suspension system and one-fourth of the vehicle mass as a point mass.

Half simple discrete vehicle model, which is more complicated, is shown in **Figure 2**. It contains left or right half of the vehicle model.

Full simple discrete vehicle model is the most complicated version of the simple discrete models and is shown in **Figure 3**.

In simple discrete models, M represents the mass of the vehicle body without suspension system and tires, I represents the inertia of the vehicle without suspension system and tires. mi2 and Ii2 represent relevant suspension system's mass and inertia, respectively. mi1 and Ii1 represents relevant tire's mass and inertia, respectively. Suspension system's and tire's stiffness and damping properties are represented with k and c, respectively. In this simple discrete model, main body of the vehicle is represented by a rigid body that has a single mass and inertia. Each one of the four suspension systems is represented with a point mass and inertia. Similarly, each one of the four tires is represented with a point mass and inertia. Suspension system and tires

**Figure 1.** *Quarter simple discrete vehicle model [2].*

**Figure 2.** *Half simple discrete vehicle model [3].*

**Figure 3.** *Full simple discrete vehicle model [4].*

are represented by single stiffness and damping coefficients. Qi1 is the excitation applied from the road [5, 6].

Quarter simple discrete vehicle model should be used for initial analysis. For further analysis, a half simple discrete vehicle model should be used for more accurate results which are also closer to the full simple discrete vehicle model when compared to dynamic responses [2]. Subsequently, a full simple discrete vehicle model should be used for more accurate results. MATLAB can be preferred as a solver of equations of motion.

#### *2.1.2 Flexible multi-body model*

Flexible multi-body models are more complicated when compared with simple discrete models since they have rigid and flexible bodies. In this method, system is modelled with rigid and flexible bodies connected by joints. In this method, solution can be calculated by deriving below fundamental equations [7].

Newton's equation using D'Alambert's principle for particle i is shown in Eq. (3).

*Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

$$
\overrightarrow{F\_i} - m\_i \overrightarrow{r\_i} = \mathbf{0} \tag{3}
$$

where,

*ri* !: Position vector of particle i

*Fi* !: Sum of forces acting on the particle

Virtual work which is done by these forces is also zero.

$$
\delta V\_i = \left(\overrightarrow{F\_i} - m\_i \overrightarrow{r\_i}\right) \delta \overrightarrow{r\_i} = \mathbf{0} \tag{4}
$$

where,

*δVi*: Virtual work

*δ ri* !: Virtual displacements of the particle i

$$
\overrightarrow{F\_i} = \overrightarrow{F\_i} + \overrightarrow{F\_i} + \overrightarrow{F\_i} + \overrightarrow{F\_i} \tag{5}
$$

where,

*Fe i* !: External forces acting on the particle i

*Fs i* !: Internal forces from other particles in the same body

*Fr i* !: Joint reaction force from another movable body through a joint

*Fd i* �!: Reaction forces due to joints with the fixed frame

$$\sum\_{i=1}^{\infty} \left( \overrightarrow{F\_i} - m\_i \overrightarrow{r\_i} \right) \delta \overrightarrow{r\_i} - \sum\_{j=1}^{n} \delta \mathbf{s}\_j = \mathbf{0} \tag{6}$$

where,

n: number of flexible bodies

*Fe i* !: External forces acting on the particle i

*mi*: Mass of the particle i

*r*€*i* !: Acceleration vector of the particle i

*δ ri* !: Virtual displacements of the particle i

*δsj*: Virtual change in strain energy of flexible body j

Lagrange form of D'Alambert's principle is shown in Eq. (7).

$$\sum\_{j=1}^{n} \left( f\_j + f\_j^{\circ} \right) \delta q\_i + \sum\_{i=1}^{\infty} \overrightarrow{F\_i^{\circ}} \delta \overrightarrow{r\_i} = \mathbf{0} \tag{7}$$

where,

*fj* : Generalized external force corresponding to general coordinate j

*f s j* : Generalized structural stiffness forces

*δqi* : Virtual displacement in independent coordinate i

*F* ∗ *i* �!: Inertia method of particle i

*δ ri* !: Virtual displacements of the particle i

For a system of rigid or flexible bodies generalized inertia forces can be calculated as follows.

$$f\_j^\* = -\sum\_{k=1}^N \left[ \rho \overrightarrow{r^k} \frac{\overrightarrow{\partial r^k}}{\partial \dot{q}\_j} dV \right] \tag{8}$$

*f* ∗ *j* : Generalized inertia forces j

N: Number of bodies

*ρ*: Density

*r* !*k* : Position vector of an arbitrary point in body k

*qj* : Independent coordinate j

V: Volume

#### **2.2 Finite element model (FEM)**

After analytical models created and detailed analyses are required, detailed models should be created such as finite element models. Finite element method is a powerful and popular method that provides numerical solutions of differential equations. In this section, finite element method will be handled for dynamic and structural analysis. FEM divides whole domain into smaller divisions which are named finite elements in order to solve the problem. This method converges a solution by minimizing an error function.

Advantages of FEM are as follows:

1.FEM represents a complicated model more accurately.

2.FEM represents the total solution of the whole model more accurately.

3.FEM provides more detailed solutions for structural and dynamic analysis.

4.FEM requires less assumption and simplification.

Disadvantages of FEM are as follows:

1.FEM causes more time and computational cost.

2.FEM causes more modelling effort.

FEM of a vehicle can be classified in three groups such as simplified FEM, reduced degrees of freedom FEM and detailed FEM. At the beginning of the design phase, obtaining faster results is the most important thing since lots of iterations are needed to get the final design. Therefore, simplified and less degrees of freedom models should be preferred at the beginning. When desired response is achieved, detailed and more degrees of freedom models should be used for more accurate results. Detailed models are particularly required for structural analysis. Simplified and approximate models are inadequate for structural analysis. On the other hand, detailed models are not convenient for iterative solutions.

#### *2.2.1 Simplified FEM*

Simplified FEM generally consists of simple beam, truss, shell elements (As a rule of thumb smallest dimension is at least 20 times less than the largest dimension), point

#### *Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

masses and rigid body elements to bind them. When creating shell elements, midsurfaces should be created at first. Then mid-surfaces should be attached to each other at the same level for continuity. Subsequently, shell elements should be created by defining the thickness and mid-surface layer position. This process is critical for creating shell models.

The purpose of creating simplified FEM is to obtain quick and approximate results to anticipate how close the first approach to the final one is. Dynamic response of the vehicle can be approximately calculated by using these models. Moreover, simplified FEM has particularly huge advantage over detailed FEM when the past experience over vehicle design is weak or the dynamic load acting on the system is quite new.

Simplified FEM should be created by using an automated code since making modifications for every iteration will take time. Therefore, simplified FEM should be built as parametric. Easily modifiable parts should be modelled as parametric, and modelling and post processing codes should be automated.

Cicek et al. [8] created a simplified FEM to investigate the individual effects of a heavy vehicle's design parameters. Simplified Fem is shown in **Figures 4** and **5**.

There are other ways to create and analyse the system response such as gathering mass and stiffness matrix of a vehicle by using a FEA software and calculate system response by using analytical methods and these matrices. This method should be preferred for much simpler models since gathering these matrices will be much harder according to the complexity of the vehicle model. These matrices can be used to perform modal analysis only which is the first step of figuring out of the dynamic behaviour of the vehicle. On the other hand, computation time will increase due to matrix inversion when vehicle model has more degrees of freedom. Moreover, for further dynamic analysis numerical approach is better.

The other way is using superelements. A superelement is a special type of finite element which consists of a group of finite elements. It is particularly employed to simplify the model when most of the model remains the same after modifications. A simple example of a superelement is shown in **Figure 6**. Left part of the example represents the parts that will be modified during design iterations. Right part of the

**Figure 4.** *Simplified finite element model [8].*

**Figure 5.** *Simplified finite element model's mesh [8].*

#### **Figure 6.**

example represents the superelement that will remain the same during the design iterations. Exterior nodes represent the connection points. By using superelement approach, the number of degrees of freedom of the superelement part will be reduced which will provide a significant computation time due to the fact that analysis of the superlement part will not be calculated for every iteration [10]. This will provide a huge advantage during the first part of the vehicle design phase. On the other hand, reduction process will cause a computation cost. Superelement approach is applicable when the number of exterior elements is much less than the interior elements in the superelement. Furthermore, this method can be only utilized for figuring out the dynamic behaviour of the vehicle system like other simplified models. Moreover, the theory behind this technique should be comprehended before deciding to employ it.

#### *2.2.2 Reduced DOF FEM*

Reduced DOF FEM is another method to obtain faster results since it has hundreds of DOF when simplified FEM has thousands of DOF. Therefore, component modal synthesis (CMS) [11] which is the most popular reduction method is handled. This method employs highly truncated mode set by converting finite element model's equations of motion into modal domain in other words generalized coordinates space. The usage of this method is an improved version of superelement method. CMS is developed by using Craig Bampton method [12].

*A simple superelement example [9].*

*Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

$$
\mu = Tq \tag{9}
$$

where,

*u*: System response which consists of many DOF

*T* : Transformation matrix

*q*: Modal coordinates consist of less coordinates

$$u = \Phi q \tag{10}$$

where,

*Φ*: Mode shape matrix which is obtained by below equation

$$\det\left(K - \lambda^2 M\right) = 0\tag{11}$$

$$M\ddot{u} + Ku = F \tag{12}$$

where, *M*: Mass matrix *K*: Stiffness matrix *F*: Force matrix

$$
\Phi^T \mathbf{M} \Phi \ddot{q} + \Phi^T \mathbf{K} \Phi q = \Phi^T \mathbf{F} \tag{13}
$$

By using above equations, N DOF finite element model can be converted into n DOF model where N is much greater than n. The main issue is defining n since if it is selected closer to N, reduction will be pointless. When n is selected as smaller there is risk for truncated model that cannot represent the main model adequately. Therefore, modal effective mass approach can be utilized. This method calculates the contribution of each mode on rigid body motion. As a rule of thumb, modal effective mass should be more than 90%. Moreover, highest natural frequency should be selected more than 2 or 3 times higher than excitation frequency for reduction. Furthermore, number of attachment nodes should be taken into account when calculating the minimum number of modes required for reduction. It should be noted that reduction will bring truncation some errors [13].

Semi-trailer finite element model is shown in **Figures 7** and **8** as an example.

**Figure 7.** *Semi-trailer finite element model.*

**Figure 8.** *Semi-trailer chassis finite element model.*

#### **Figure 9.**

In semi-trailer finite element model, suspension system will be fixed to the lower chassis.

In this example semi-trailer model, suspension system is connected from reduced DOF FEM from its external nodes. Suspension system is modelled as rigid bodies in this example that is shown in **Figure 9**. Moreover, spring, damper and tire elements are utilized to represent stiffness and damping characteristic of the suspension system and tires. Tire properties can be acquired from the producer or can be obtained by conducting tests. Furthermore, road should be modelled for a complete dynamic analysis. Road can be modelled as 2D or 3D. Tire and road properties and their total dynamic behaviour should be verified for more realistic results. Pure and coupled reaction forces applied from road to tire and slip conditions are also should be taken into account when defining road and tire properties for an analysis model.

#### **3. Dynamic analysis of a vehicle**

After modelling of a vehicle process completed dynamic analysis process starts. All assumptions, simplifications and modifications effect on the dynamic response of the

system. Therefore, after verification of the dynamic analysis models should be reviewed and next iteration should be initiated after necessary modifications made on the vehicle model.

Particularly for the finite element models, at the very beginning of the dynamic analysis vehicle model should be elaborately reviewed such as finite element mesh continuity, common nodes, element shape and size, material properties, thickness definition for the . In order to reveal that firstly free-free modal analysis should be performed. As a result of the modal analysis local modes appear if mesh continuity could not accomplished. Moreover, rigid body modes can be seen if connection of some parts are missing. As a result of modal analysis, aforementioned mistakes should be resolved for a proper dynamic analysis.

When the finite element model is convenient for the dynamic analysis, modal analysis should be performed. Modal analysis will provide overview about dynamic characteristic of the vehicle model [14]. Bending mode of a truck chassis is shown in **Figure 10**.

Furthermore, modal analysis results should be compared with the analytical model results. Natural frequency order of magnitude and mode shape should be close to the analytical model results particularly for the first bending mode. After getting experience about the vehicle, the need for the analytical model will disappear. Moreover, modal effective mass results should be reviewed. This will give the idea of which mode will contribute when reduction is applied.

After modal analysis, static analysis should be performed by applying boundary conditions and gravity load on the system. Static analysis is commonly considered the first step of the structural analysis. The static analysis of vehicle parts is utilized to obtain the effects of constant gravity loads on the vehicle without taken into account the inertia and changing dynamic loads. After static analysis stress, strain and deformation of the parts can be obtained under a constant load. To figure out the deformation results better exaggerated views can help. Simple discrete models and simplified models can talk less about the static analysis results since they have less details. On the other hand, detailed finite element model should be employed to investigate stress, strain and deformation of the parts. In the detailed FEM, elaborated structural analysis can be performed on parts such as bolts, nuts and welding points. Every detail will

**Figure 10.** *Modal analysis of a truck chassis.*

take some time for modelling and will cause a computation cost. Therefore, static analysis of the detailed FEM should be performed for the last iterations.

Up to now, natural frequencies and mode shapes are calculated by using simple discrete model and simplified FEM. Modal analysis results are compared for the simple discrete model and simplified FEM. Modifications on the simplified FEM are applied if needed. Static analysis is performed for the simple discrete model and simplified FEM. Results are compared. Now we are sure about our models, assumptions and simplifications and it is time to perform dynamic analysis.

First step of the dynamic analysis is to model the dynamic load on the vehicle. Dynamic load can be caused by the road, engine, transmission and etc. Dynamic load can be acquired by test results or can be modelled. After dynamic load is acquired, damping characteristic of the vehicle should be defined as a second step. Damping characteristic can alter material properties and joints of the vehicle model. For a linear elastic system proportional damping can be employed [8].

$$[\mathbf{C}] = \mathbf{x}[\mathbf{M}] + \beta[\mathbf{K}] \tag{14}$$

where,

½ � *C* : Damping matrix

½ � *M* : Mass matrix

½ � *K* : Stiffness matrix

∝ and *β* are the constants of proportionality

Dynamic analysis should be performed by using simple discrete model, simplified FEM and reduced DOF FEM as a third step. Dynamic analysis results should be compared to figure out the dynamic response of the system. Simple discrete model and simplified FEM can provide dynamic response of the system as an order of magnitude. For a more realistic solution reduced DOF FEM should be used. In order to use the reduced DOF FEM, vehicle model should be continuous, linear elastic and deformations should be small (about 10%) due to the fact that CMS is based on linear theory. Reduced DOF FEM is a commonly preferred model to calculate the dynamic response of a vehicle. On the other hand, for larger deformations and nonlinear structures, reduced DOF FEM is not valid. Moreover, to calculate stress and strain, detailed FEM should be preferred since reduced DOF FEM is not a powerful method since it solves the matrices in modal domain.

After reduced DEF FEM dynamic analysis is performed, dynamic characteristic of the vehicle is obtained and for elaborated analysis detailed FEM should be utilized. Detailed FEM and reduced DOF FEM dynamic analysis results should be compared in terms of deformation, reaction forces and transmissibility. Transmissibility is the ratio of the output to the input. Transmissibility equals to 1 when the model is rigid body. However, our vehicle model has some damping, transmissibility will change according to the frequency. Therefore, reduced DOF FEM and detailed FEM should be compared in terms of transmissibility in order to obtain more realistic results. According to the system response of a vehicle, some modifications can be required to decrease the deformation and transmissibility. Same rule applies here, too. Detailed FEM should be created, and dynamic analysis should be performed as multi body simulation particularly for the last iterations. Multi body simulation is a dynamic analysis method where multi body model has all necessary structural elements. Multi body truck model is shown in **Figure 11**.

As a last step, dynamic structural analysis results should be obtained by using detailed FEM. Deformations, stress, strain, transmissibility and reaction forces should

**Figure 11.** *Multi body truck model [15].*

be calculated by using detailed FEM. Particularly for stress results, mesh size and mesh quality should be reviewed. In some situations, stress results can change according to the mesh size. As a rule of thumb, big mesh size causes bad results and small mesh size causes more computation time. Therefore, different mesh sizes should be compared to have experience on the vehicle FEM. Likewise, mesh quality is another factor that causes misleading results. Thus, mesh quality should be kept high particularly in stress concentration areas.

### **4. Verification of the analysis**

In order to rely on the analysis results, models should be verified. Testing is the most common method to verify the models. Verifying modal analysis results is the first step of the verification of dynamic analysis. Modal updating is the mostly applied method to verify the modal analysis. Modal updating works by updating mass, stiffness and damping parameters of the model to decrease the difference between the analysis and the test results. Decreasing the difference between analysis and test results more will provide a better and more realistic models. Therefore, modal test should be performed. Modal testing is applied to determine the natural frequencies, mode shapes, modal masses, modal damping ratios of a system under a test condition. Modal testing is a method that is used for characterizing the structure by delivering a known force to the structure and measuring both the force and the response of the structure. Structure's response should be measured from multiple locations to figure out the response of the system. After acquiring the measurements, the data should be processed to determine the natural frequencies and the movement of the structure. There are two famous ways to perform modal analysis such as impact hammer modal testing and shaker modal testing.

The hammer impact test is a cost efficient and the simplest method to gather the system's frequency response functions (FRFs). The main purpose of the impact hammer modal testing is to apply the structure a perfect impulse that will cause all modes of vibration by exciting the structure with equal energy. On the other hand, the hammer impact test has some restrictions. The acquired data may have noise that cause inaccurate calculations of modal parameters. It is impossible to perform a

perfect impulse since there is a duration of contacting the hammer with the structure. This time period effects the frequency content of the impact. Therefore, this impact should be measured and recorded with a load cell. Impact hammer testing is more convenient for small and lightweight structures. Thus, small parts of the vehicle should be preferred instead of the whole vehicle for impact hammer testing. Hammer impact test set-up is shown in **Figure 12**.

For complicated structures and obtaining more accurate results modal shaker testing is better than the impact hammer testing to identify coupled modes for closer frequencies. Full vehicle modal test set-up is shown in **Figure 13**.

After modal updating procedure is completed, static test verification should be performed. Reaction forces of the structure under its own weight should be calculated and reaction forces of the structure should be compared with the test results. Load cells are mostly used test equipment for the static tests. Moreover, deformations should be measured and compared with the static analysis results.

**Figure 12.** *Hammer impact test set-up [16].*

**Figure 13.** *Full vehicle modal test set-up [17].*

#### *Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

After static analysis, dynamic load can be applied on the vehicle. Different dynamic load scenarios that are generated from user experiences and use cases should be applied to the vehicle model to assess subsystems and components effects on the whole vehicle model. Vehicle performance is obtained by applying these scenarios. Dynamic load can be either driving loads or dynamic force that acts on the static vehicle. Dynamic load is advised directly applied on the structure by omitting tire and road effect as a first step. Deformations and strain data should be acquired to figure out the system's dynamic response. After that, dynamic test results should be compared with the dynamic analysis results. Modifications should be made on the vehicle model for verification. As a second step, tire and road effects should be added to the tests. Tire and road models are critical for verification. Therefore, tire and road model should be verified separately and together before dynamic analysis. Commercial analysis software's experience can be utilized to model tire and road. However, some modification may be needed for verification. During dynamic tests load, torque, velocity and displacement data can be acquired. Measurement locations is the critical point for verification. Therefore, main target and other aims of this vehicle should be determined at the beginning. If the main target of a vehicle is to carry passengers then comfort of the passengers should be maximized. Vibration acting on the passengers should be measured, and design should be modified to minimize the disturbance of the passengers. In this scenario, vehicle design should be modified according to passenger riding comfort and verifications should rely on riding comfort. Moreover, control algorithms can be added to the vehicle dynamics to increase passenger's riding comfort. In another scenario, vehicle can be truck that carries critical cargo. In this situation, main target of the vehicle is cargo. In another scenario, vehicle can be a working machine. In this case, dynamic load applies on the vehicle when vehicle is static. As a result, dynamic test measurements should be both acquired by considering verification of the analysis model and verification of the targets of the vehicle. Once the vehicle model and analysis results are verified, modifications on the design or test conditions can be changed and there will be no need to verify vehicle model again unless these modifications are out of verification. Dynamic test of a vehicle is shown in **Figure 14**.

In this chapter, modelling of a vehicle, implicit dynamic analysis with linear elastic deformation assumption and verification of the model and analysis is examined. Explicit analysis, fatigue analysis, buckling analysis, large or non-linear deformations

**Figure 14.** *Dynamic test of a vehicle [18].*

are out of scope of this chapter. In order to solve these analyses, different kind of mathematical equations must be solved, and different kind of assumptions must be made. Moreover, verification of these analyses and test methods of these conditions are totally different.

#### **5. Conclusion**

In this chapter, modelling of a vehicle, dynamic analysis methods and verification of both models and analysis are investigated. Modelling methods of a vehicle are classified in two types such as analytical models and finite element models. Analytical models are grouped as simple discrete model and flexible multi-body model. Finite element models are classified as simplified FEM, reduced DOF FEM and detailed FEM. Aforementioned models are examined before dynamic analysis process. After that dynamic analysis methods of a vehicle are elaborated. Modal analysis, static analysis and dynamic analysis of a vehicle are elaborated. Furthermore, verification methods of models and analyses are examined. Industrial experiences are employed when assessing the methods.

#### **Acknowledgements**

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#### **Nomenclature**

#### *Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

### **Thanks**

I would like to thank to ROKETSAN for funding this chapter. I am indebted to Bülent ACAR, principle engineer at ROKETSAN, for his support.

*The Dynamics of Vehicles – Basics, Simulation and Autonomous Systems*

### **Author details**

Burak Can Çiçek Roketsan Joint Stock Company, Ankara, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Modelling and Dynamic Analysis of a Vehicle DOI: http://dx.doi.org/10.5772/intechopen.107216*

#### **References**

[1] Çiçek BC. Dynamic Analysis and Modelling of a Rocket Launcher System. Ankara: Middle East Technical University; 2014

[2] Desai R, Guha A, Seshu P. A comparison of quarter, half and full car models for predicting vibration attenuation of an occupant in a vehicle. Journal of Vibration Engineering & Technologies. 2021;**9**(5):983-1001. DOI: 10.1007/s42417-020-00278-3

[3] Gao W, Zhang N, Du HP. A half-car model for dynamic analysis of vehicles with random parameters. In: 5th Australasian Congress on Applied Mechanics. Brisbane, Australia: ACAM; 2007

[4] Mudduluru SR, Chizari M. Quarter and full car models optimisation of passive and active suspension system using genetic algorithm. ArXiv. DOI: 10.48550/arXiv.2101.12629

[5] Shao K, Huang K, Duan S, Zhen S, Gao F. Modelling and dynamic analysis of vehicles based on discrete constraints. International Journal of Vehicle Design. 2017;**75**(1–4):23-46. DOI: 10.1504/ IJVD.2017.090903

[6] Yang S, Lu Y, Li S. An overview on vehicle dynamics. International Journal of Dynamics and Control. 2013;**1**(4): 385-395. DOI: 10.1007/s40435-013- 0032-y

[7] Işık Ç. Experimental Analysis and Modeling of a Missile Launcher System. Ankara: Middle East Technical University; 2012

[8] Çiçek BC, Acar B, İder S. Dynamic analysis and design optimization of a heavy military vehicle. International Journal of

Heavy Vehicle Systems. 2021;**28**(3): 309-328. DOI: 10.1504/IJHVS.2021.117502

[9] NX Nastran 10. NX Nastran Model Reduction and Superelements: Theory and Implementation [Internet]. 2018. Available from: https://www.ata-e.c om/wp-content/uploads/2021/10/NX\_ Nastran\_Superelement\_In\_depth.pdf [Accessed: August 13, 2022]

[10] Branlard E, Shields M, Anderson B, Damiani R, Wendt F, Jonkman J, et al. Superelement reduction of substructures for sequential load calculations in OpenFAST. Journal of Physics. 2020; **1452**(1):12033

[11] Hintz RM. Analytical methods in component modal synthesis. AIAA Journal. 1975;**13**(8):1007-1016. DOI: 10.2514/3.60498

[12] Mapa L, das Neves F, Guimarães GP. Dynamic substructuring by the Craig– Bampton method applied to frames. Journal of Vibrational Engineering and Technology. 2021;**9**:257-266

[13] Cuppens K, Sas P, Hermans L. Evaluation of the FRF based substructuring and modal synthesis technique applied to vehicle FE data. Proceedings of the International seminar on modal analysis. 1998;**3**:1143-1150

[14] Renuke PA. Dynamic analysis of a car chassis. IJERA. 2012;**2**(6):955-959

[15] Vehicle dynamic simulation of multi-body systems [Internet]. Available from: https://engineering.mpt.magna. com/simulation-services-software/ dynamics [Accessed: August 13, 2022]

[16] Iglesias FS, López AF. Rayleigh damping parameters estimation using hammer impact tests. Mechanical

Systems and Signal Processing. 2020;**135**: 106

[17] Oktav A. Experimental and numerical modal analysis of a passenger vehicle. International Journal of Vehicle Noise and Vibration. 2016;**12**(4):302-313

[18] Kistler [Internet]. Available from: https://www.kistler.com/en/solutions/ research-and-development-testing/ automotive-testing/vehicle-dynamicstesting [Accessed: August 13, 2022]

Section 3

## State of Art in Vehicle Dynamics

### **Chapter 3**

## Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles

*Yuan Yin*

### **Abstract**

The chapter explains the effects of intelligent autonomous vehicles from future perspectives. The chapter gives readers an overview of the future intelligent autonomous vehicles and promotes the development potential on intelligent. To be specific, the chapter first gives the readers an overview of the development of autonomous vehicles. Then, the chapter introduces the potential of intelligent autonomous vehicles, key technologies that are needed for future intelligent autonomous vehicles, and how intelligent autonomous vehicles affect the future. Finally, the chapter discusses barriers in intelligent autonomous vehicles development. The chapter will be contributed as a start point for people who want to keep working on intelligent autonomous vehicles and help them understand the general condition of future intelligent autonomous vehicles.

**Keywords:** autonomous vehicles, autonomous driving, self-driving, future, intelligence

#### **1. Introduction**

Autonomous driving is a field with a promising future [1, 2]. The human society is extremely dependent on transportation. According to statistics, there are about more than 1.4 billion vehicles in the world [3]. Tens of millions or even hundreds of millions of drivers manipulate vehicles around the world every day, which is a very large consumption of manpower [4]. Intelligent autonomous vehicles, including driverless vehicles, have become a hot topic of concern for regulators and industry in recent years. Intelligent autonomous driving has been defined as the direction of development in the next 10 years [5].

Based on the degree to which dynamic driving tasks that the intelligent automation driving system can perform, the role assigned in the execution of dynamic driving tasks, and with or without the design operating condition restrictions, the driving automation is divided into levels 0–5 (**Table 1**).

Level 0 (No Automation): Drivers are in full control of the vehicle. The automation driving system cannot continuously control vehicle motion in dynamic driving tasks, but has the ability to continuously detect and respond to partial target and event in dynamic driving tasks. Typical vehicles currently on the road are classified as Level 0.

#### **Table 1.**

*The automotive driving division elements.*

Level 1 (Driver assistance): The automation driving system continuously controls vehicle motion in dynamic driving tasks within its designed operating conditions and has the ability to appropriately detect and respond to partial targets and events that related to vehicle motion control. In other words, Level 1 automated systems are sometimes able to assist the driver in certain driving tasks.

Level 2 (Partial Automation): The driving automation system continuously controls vehicle motion in dynamic driving tasks within the conditions for which it is designed to operate and has the capability to detect and appropriately respond to some of the targets and events that are related to vehicle motion control. In other words, the automation system is capable of performing certain driving tasks, but the driver needs to monitor the driving environment and complete the remainder. Also, drivers need to take over driving when problems arising at any time. At this level, the automation system asks the driver ready to correct errors in perception and judgment, which has been able to be provided by most of car companies. Level 2 condition can be split by speed and environment into different usage scenarios, such as low-speed traffic jams on ring roads, fast travel on highways, and automatic parking with the driver in the car [6]. Most of the new vehicles on the market are classified as Level 1 or Level 2. These new vehicles are equipped with assisted driving features such as lane centering and speed control, which are useful for both parking and driving [7]. However, the car remains in the hands of the driver.

Level 3 (Conditional automation): The automation system is capable of both performing certain driving tasks and monitoring the driving environment in certain situations, but the driver must be ready to regain driving control when they are requested to do it by the automated system. At this level, drivers are still unable to sleep or take a deep rest. Currently, the highest level that commercial vehicles can reach is at Level 3 at most [8]. Conditional automation requires that the vehicle be able to drive autonomously under ideal conditions, such as at a given speed and road type, but there are many limitations when these conditions are exceeded. The meaningful deployments seen so far are upgraded high-speed road condition.

Level 4 (High Automation): Automation systems are able to perform driving tasks and monitor the driving environment in certain environments and under specific conditions. Specifically, the driving automation system performs all dynamic driving tasks continuously and takeovers dynamic driving tasks under the designed operating conditions. Under this stage, in the range of automatic driving can operate, all tasks related to driving and the driver have nothing to do. The perception of external

*Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles DOI: http://dx.doi.org/10.5772/intechopen.107486*

responsibility is all in the automatic driving system. Level 4 vehicles are almost fully automated, but their automation systems can only be used in known use cases. They cannot be applied in driving off-road or in extreme weather conditions. In these unknown situations, the driver must steer the vehicle. Most of deployments of Level 4 are currently set in urban-based condition [9]. It is expected for fully automated valet parking or can be done directly in conjunction with a taxi service.

Level 5 (Fully Automation): The automation system can perform all driving tasks under all conditions. Specifically, the driving automation system continuously performs all dynamic driving tasks and takeovers dynamic driving tasks under any drivable condition. Level 5 vehicles are truly driverless cars. To reach Level 5, a vehicle must be able to navigate autonomously through any road condition or hazardous obstacle.

Level 1 and Level 2 cannot be considered autonomous driving; instead, they are Advanced Driver Assistance Systems (ADAS) [10]. Level 3 and above can be called autonomous driving. The majority of Level 3 vehicles is still test vehicles and is not really commercially available [11]. The current autonomous driving is still not perfect. Although the car is in automatic situation, the driver must be alert and ready to take over the vehicle to deal with accidents. It can be phased, in the future, autonomous driving will become a mature and reliable technology. It is not yet mature, but it will have a bright future.

#### **2. The development of autonomous vehicles**

#### **2.1 Development history**

In August 1925, a radio-controlled car called "American Miracle" was unveiled by Francis P. Houdina, a U.S. Army electrical engineer [12]. By radio control, the steering wheel, clutch, brakes, and other parts of the vehicle can be remotely controlled. According to the New York Times, the radio-controlled vehicle can start its engine, turn its gears, and honk its horn like a phantom hand at the wheel. It's a long way from "automation driving," but it was the first documented self-driving vehicle in human history.

In 1939, General Motors exhibited the world's first self-driving concept vehicle— Futurama—at the New York World's Fair [13, 14]. Futurama is an electric car guided by a radio-controlled electromagnetic field, which is generated by magnetized metal spikes embedded in the road. It was until 1958, however, that General Motors brought this concept vehicle to life [14]. General Motors embedded sensors called pickup coils in the front of the car. The current flowing, which goes through the wires embedded in the road, could then be manipulated and tells the vehicle to move the steering wheel to the left or right [15].

During the next close to 20 years, the development of autonomous driving technology hit a bottleneck and developed more slowly, with no significant progress. It was until the 1970s, especially after computers and IT technology began to develop at a rapid pace, that autonomous driving technology again ushered in a period of rapid development. In 1977, Japan's Tsukuba Mechanical Engineering Laboratory improved the pulse signal control method previously used by General Motors. They used a camera system that forwards data to a computer to process road images. This allows the car to follow white road signs at 30 kilometers per hour on its own, though it still needs the assistance of steel rails. A decade later, the Germans improved the camera

and developed VaMoR. A vehicle equipped with the camera could drive safely at 90 km/h [16]. As technology has advanced, the environment detection and reaction ability of self-driving cars also has been improved.

In 1984, the Defense Advanced Research Projects Agency (DARPA), in partnership with the Army, launched the Autonomous Ground Vehicle (ALV) program [17]. The goal of this program is to give vehicles full autonomy and allow them to detect terrain through cameras and calculate solutions such as navigation and driving routes through computer systems. The second DARPA Challenge in 2005 was the first time in history that five driverless cars successfully navigated a desert track with rough road conditions using a recognition system.

Since 2009, Google has been secretly developing a driverless car project, which is now known as Waymo [18]. In 2010, it was reported that Google was testing self-driving cars at night on Highway for 1000 miles without human intervention and that occasional human intervention had been required for a total of more than 140,000 miles [19]. In 2014, Google demonstrated a prototype of a driverless car without a steering wheel, gas or brake pedal, resulting in 100% self-driving [20]. As driverless program steadily advances, the potential and opportunities of "autonomous driving" are being discovered by more and more people.

#### **2.2 Industrial**

In recent 10 years, self-driving cars have become a key area of concern for many companies [21]. There are two evolutions of autonomous driving industry. The first-time evolution is triggered from the expectation of autonomous-driving industrialization. There are many landmark events, such as Waymo's spin-off, General Motors' acquisition of Cruise, Ford's investment in ArgoAI [22]. The background of this evolution is that artificial intelligence technology has improved tremendously, and deep learning has started to be applied on a large scale in the field of perception technology, such as image recognition [23]. Also, sensor technology has developed greatly [24]. However, at that time, the long tail problem has become an important constraint for the implementation of high-level autonomous driving. Specific challenges exist in the robustness of hardware, redundancy of the system, and perfection of testing [25].

The second evolution comes from the commercialization of autonomous driving. After 3–4 -year development of technology, the core technologies of autonomous driving such as LiDAR, chips, perception, and decision algorithms have further developed [26, 27]. Autonomous driving in specific scenarios (such as mines, ports, and airports) has been able to be commercialized.

At present, the global autonomous vehicle industry is developing in a better trend. However, few areas can achieve mass production [28]. Autonomous vehicle technology is developing together with 5G communication technology and related technology of new energy vehicles.

#### **3. Why intelligent autonomous vehicles have a huge potential**

Autonomous vehicles technology has the potential to transform commuting and long-distance travel experiences, take people away from high-risk work environments, and allow for a higher degree of development and collaboration across industries. It is also the key to building our cities of the future.

*Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles DOI: http://dx.doi.org/10.5772/intechopen.107486*

#### **3.1 Living**

In the future, human's relationship with vehicles will be redefined from reducing carbon emissions and paving the way for more sustainable lifestyles levels. It is estimated that 30% of greenhouse gas emissions in the United States come from transportation [29]. Autonomous vehicles will be able to travel more efficiently on the road than vehicles operated by human drivers. This will lead to a reduction in greenhouse gas emissions. Also, vehicles can be grouped into "platoons" and reduced number of accidents. These can keep traffic flow continuously. Less congestion means that passengers can get to their destinations faster and spend less time on the road, which in turn makes more fuel efficiency and reduces CO2 emissions. Vehicles will also likely communicate with road infrastructure, such as traffic signals, to adjust fuel consumption and emissions accordingly.

Cab services, car sharing, and public transportation will become faster and cheaper as the autonomous vehicles revolution progresses. The cost of these services is therefore expected to decrease as maintenance, gas, and labor requirements decrease. In this way, the cost gaps between purchasing one's own car and using these travel services will be stretched to a degree that will redefine how people travel. More importantly, the reduced cost of transportation services will drive economic mobility for vulnerable populations. Geographic locations that previously have been inaccessible to certain populations due to commuting costs will become accessible, resulting in some new beneficial effects on the working population.

When autonomous vehicles become available, navigation will be more efficient and traffic congestion will be reduced. Also, because cabs and car-sharing services will be cheaper than purchasing a car, there will be fewer cars on the road. These will ultimately improve commuting efficiency. The saved commute time can be spent on work, socializing, and relaxing. This thus reduces the anxiety commuters when they arrive at work and being able to work in a better condition. In addition, the reduction in travel time also helps to improve daily productivity.

#### **3.2 Human nature**

Laziness is often the first driver of technological progress. Looking back at the history of technological progress, people have invented home appliances in order to reduce the labor of household chores. To save the pain of walking, human beings have made carriages, bicycles, motorcycles, cars, trains, and airplanes. Humans hate repetitive and inefficient tasks.  Therefore, they let the tools do it and automate the repetitive tasks. Driving is a relatively repetitive and inefficient task. Developing tools to achieve automation is clearly in line with the trend of technological development. From efficiency perspective, the popularity of intelligent autonomous vehicles can improve efficiency and save time.

#### **3.3 Safety**

Safety of intelligent autonomous vehicles is defined as their ability to keep people safe and reduce the accident rates when autonomous vehicles are operating properly. Research shows that intelligent autonomous vehicles are safer than the average human drivers [30]. Firstly, machines are much better at perception than humans. The autonomous vehicle has various sharp sensors, radar, and cameras, which can perceive a wider range than the human when driving. The hardware upgrades allow

vehicles to "see" the world that humans cannot see in a farther, wider, and clearer way. Also, it can see many different angles at the same time, which is beyond the range of human perception. For example, Tesla has eight cameras mounted around the body (two in the front, two on the left and right side, and two in the rear), providing a 360-degree view and a range of 250 meters [31]. Front-enhanced radar is installed to provide clearer and more accurate detection data in adverse weather conditions (such as rain, fog, and smog). All of these are beyond the ability of human perception. Therefore, autonomous vehicles can make decisions earlier than humans and react faster, which makes driving safer. Furthermore, for, autonomous vehicles, vehicle-tovehicle communication will be possible. There will be more communication in various scenarios and further improve safety.

Secondly, autonomous vehicles are more energetic than humans. Globally, fatigue driving has become one of the major causes of traffic accidents. According to the National Highway Traffic Safety Administration, there are about 100,000 traffic accidents on U.S. roads each year due to drivers going to sleep while driving [32]. Studies show that approximately 94% of crashes are caused by human error. The World Health Organization estimates that more than 1.3 million people die in road traffic accidents each year [32]. The number of deaths and injuries from car accidents caused by distracted drivers continues to increase [32]. Autonomous vehicles do not need a human driver. Instead, the driver is a microcomputer, which runs a large amount of computer code and connects to different sensors inside and outside the vehicle. The data and sensors are connected to the cloud and can simulate the external environment around the vehicle real time. In this way, the intelligent autonomous vehicles can anticipate the actions that need to be taken based on the current surrounding traffic conditions. These actions are performed normally regardless of the climate, environmental, and traffic conditions. People get fatigued while autonomous vehicles do not. Thus, safety levels of autonomous vehicles driving are higher than human drivers.

Thirdly, autonomous vehicles are more rational than humans. People have emotions. They may make dangerous actions because of panic and rage. Autonomous vehicles do not make these mistakes, which is a major advantage of autonomous vehicles. At present, autonomous vehicles may be worse than humans in making decisions, especially in the face of various extreme situations and uncertainty. However, this piece is constantly improving. One aim of intelligent autonomous vehicles research is to improve the ability of intelligent autonomous vehicles to deal with various extreme situations and cover a variety of possible extreme cases to increase safety of intelligent autonomous vehicles. In addition, the optimal future situation is that all vehicles are intelligent autonomous, which makes most of the driving behavior predictable.

It is also acknowledged that autonomous vehicles cannot guarantee a 100% safety rate. They will have failures and flawed algorithms. However, humans cannot achieve a 100% safety rate either. As long as intelligent autonomous vehicles outperform humans, intelligent autonomous vehicles will help reduce the deaths in traffic accidents worldwide each year.

#### **4. Key technologies needed for future intelligent autonomous vehicles**

From the future perspective, intelligent autonomous vehicles will develop from low speed to high speed, from carrying goods to people, from commercial to civil use. Autonomous vehicles are a complex engineering system that requires the integration

#### *Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles DOI: http://dx.doi.org/10.5772/intechopen.107486*

and precise cooperation of various technologies, which include algorithm, client system, and cloud platform [33]. The algorithm includes sensing, which is used to extract meaningful information from the collected sensor raw data; positioning, which is used to precisely control the driving direction of the intelligent autonomous vehicles; perception, which is used to understand the surrounding environment of the vehicle and provide safe and reliable planning for the vehicle's travel and arrival. To achieve these algorithms, professionals are expected in the following areas: computer vision (including image classification, target detection, target recognition, and semantic segmentation), Kaman filtering (including vehicle position prediction, measurement, updating, and multiple sensor data fusion), Markov localization (including vehicle motion models for vehicle localization, Markov processes, Bayesian principles and position filtering, target observation localization, and particle filtering), vehicle control decisions (PID control; including lane departure error control, PID hyperparameter adaptive adjustment), model predictive control (MPC; including vehicle dynamics, vehicle trajectory prediction, finding optimal execution parameters such as steering angle and acceleration) [23, 34, 35]. Client system consists of operating system and hardware system, which can cooperate with the algorithm part to meet the requirements of real-time, reliability, safety, and green energy consumption. Cloud platform provides offline computing and storage functions to support testing of continuously updated algorithms, generating high-precision maps and large-scale deep learning model training.

#### **4.1 Scenario operation**

The future of intelligent autonomous vehicles driving also requires the cooperation of technology and scenario operation. The structured scenario can already realize Level 4 intelligent autonomous vehicles operation. However, different scenario has different requirements for technical details. Mining scenario has magnetic field interference, poor GPS signal, and harsh working environment with more dust [36]. Port scenario often encounters rain and wet weather [37]. Airport logistics needs to require high security [38]. Under these conditions, intelligent autonomous vehicles need to be connected to the overall operation management and dispatching system. Therefore, a deep understanding of the scenario and targeted changes to the algorithm are expected to achieve the implementation of intelligent autonomous vehicle driving. Also, continuous data acquisition through operation is needed to continuously iterate on the technology.

#### **4.2 Machine learning**

Machine learning behind autonomous driving is an interesting problem. Dataset of autonomous driving is on-policy, meaning that it changes with the driving strategy. Also, not all data are useful. For autonomous driving, a lot of data are monotonous and repetitive, such as nice weather, and no cars or pedestrians around, which do not help much to improve the driving strategy. How to overcome the shortcomings of each sensor to provide real-time, accurate, and non-redundant environmental information is a further requirement of deep learning.

Another area that future needs to solve is the learning improvement of driving strategy. Assuming that the driving strategy performs poorly at the beginning and requires human intervention every kilometer, for every kilometer some important data are collected, such as video and radar data, which are associated with a few seconds before the accident. Then, data are used to train the current model and learn a better strategy to avoid accidents. With better strategies, manual intervention is

reduced once for every 10 kilometers. Then, the data are used to train the current model again. The learning cycles thus are built up. From the learning, it will be found that the better the quality of the driving strategy it is, the less frequent the manual intervention occurs, the less effective training data can be gotten, and the harder it is to continue improving. As the quality of driving improves, the decay in the rate of manual intervention is very slow. The key question then is whether it can surpass the level of a real driver? The real scenarios have a variety of strange and bizarre situations. There will be some corner case never seen by the algorithm [39]. As long as the machine learning algorithm still needs a lot of data, the driving strategy seems difficult to achieve human levels. Some people think that the problems can be solved by adding more sensors [40]. However, with the increase of sensors, at first the effect of signals will raise. But then the deployment, maintenance, and coordination costs will rise significantly.

The current research on perception algorithms is mainly focused on vision. The research on applying deep learning for target recognition, segmentation, and tracking is very hot [41, 42]. However, what will happen if there is a person wearing a t-shirt painted with a stop sign on the road? More problems happen on the counter-sample. Stop-sign with a few sticky notes may be recognized as yield sign. Printing a few special patterns on the clothes may be stealthy. These are still considered countersample on the visual level. In the future, strategy-level counter-sample problems may generate with more self-driving cars becoming available.

Using artificial intelligence to make decisions is also imperative. At present, a series of research studies on decision making such as deep augmented learning, predictive learning, and imitation learning are gradually carried out.

#### **4.3 Sensors**

Autonomous vehicles require the use of multiple sensors. This at the same time is the challenge for autonomous vehicle—how to balance these different sensing modalities [43]. To overcome this challenge, an intelligent way is expected to combine the various sensors to provide a safe, high-quality autonomous driving system without significantly increasing size, weight, power, or cost [6]. The improvement of vehicle sensing focuses on collecting data from individual sensors and applies sensor fusion strategies to maximize complementary and compensate the weaknesses of different sensors in various conditions [6]. For example, cameras are excellent at recognizing signs and colors, but perform poor in bad weather or low light environments. These weaknesses can be compensated by radar [44]. However, the current systems are mainly independent, with little interaction between individual sensing systems. Also, no single sensing approach can be applied to all applications or environmental conditions.

#### **4.4 Road condition recognition**

For road condition recognition, vehicles need to control and recognize surrounding obstacles, traffic signals, pedestrians, and other vehicle states. Inertial Measurement Units (IMUs) can detect sudden jumps or deviations caused by potholes or obstacles. Through real-time connectivity, these data can be sent to a central database and used to warn other vehicles about potholes or obstacles [45]. The same is true for camera, radar, LIDAR, and other sensor data [46]. These data are compiled,

analyzed, and fused so that the vehicle can use it to make predictions about its driving environment. This allows the vehicle to become a learning machine and is promised to make better and safer decisions than humans. To date, however, very few intelligent autonomous vehicles have been able to achieve it.

#### **4.5 Storage**

The data of intelligent autonomous vehicles are massive. Data of intelligent autonomous vehicles are mostly images, sound, and other natural information. They are unstructured data, whose data volumes are much larger than structured data. In addition, intelligent autonomous vehicles require a very high level in real time. According to calculations, the amount of data collected by autonomous driving technology is as high as 500GB to 1 TB per hour per vehicle. The core of data flow in vehicles is the storage of data. The memory capacity of general computers is in a few or dozens of GigaByte (GB). The memory capacity of large servers is in hundreds of GB. It is nearly impossible to store all the data into the computer of intelligent autonomous vehicles considering the memory for computing capabilities under the existing hardware system. A potential solution is to load the data into high-efficiency hard disk storage and send it to the memory and CPU or GPU for computation when needed [47].

Another problem encountered in data storage for autonomous vehicles driving is environment [36–38, 48]. A computer hard drive is a fragile component. However, vehicles are often used in harsh environment. When the vehicle is on the road, it will experience constant vibration, extreme weather, and even sudden power outages or accidents. The hard drive, which is the "data tank" for autonomous driving, must ensure efficient and stable operation under all these circumstances. Otherwise, the data will not be sent to the computing unit, which will lead to serious consequences.

#### **4.6 Electromagnetic interference (EMI)**

There are two types of electromagnetic interference (EMI) emissions: conducted and radiated [49]. Conducted emissions are connected to the product through wires and alignments [49]. Since the noise is limited to a specific terminal or connector in the design, in the early development process, with the help of a good layout or filter design, it usually can be relatively easy to ensure compliance with the conducted emission requirements [50]. However, radiated emissions are in a different condition. Anything on a circuit board that carries current radiates electromagnetic fields. Every alignment on a circuit board is an antenna, and every copper layer is a resonator. Things, other than a pure sine wave or DC voltage, will generate noise across the signal spectrum. The power-supply designer does not know how bad the radiated emissions will be until the system is tested. To make things worse, radiated emissions can only be tested formally after the design is essentially complete. People often use filters to attenuate a specific frequency or a certain frequency range of signal strength, in this way to reduce EMI [51]. In addition, the energy of radiation, which goes through the space propagation, can be attenuated by adding metal and magnetic shielding. EMI cannot be eliminated, but can be attenuated to a level acceptable to other communications and digital devices. This is what the future needs to further detect.

#### **4.7 Chips**

Under the trend of "software-defined car," chips, operating systems, algorithm, and data form the closed loop of intelligent autonomous vehicle computing ecology. Chips are the core of intelligent autonomous vehicle ecological development. This also gives rise to a strong demand for autonomous vehicles chips. The value of chips in the autonomous vehicles is not small. The value of all the chips in a cell phone is a hundred dollars, but the value of all the chips in autonomous vehicles is up to hundreds of dollars. At the same time, there are many kinds of chips above the autonomous vehicles. Family cars need hundreds of chips, such as tire pressure monitoring, sunroof, lights to back-up camera, back-up radar, and remote control keys. When any chip misses, the car cannot be delivered.

In 2022, the global "chips shortage" still has not been effectively alleviated. The most shortage of chips is MCU chips, which is the most common chip on the autonomous vehicles. Window control, seat control, and Electronic Control Unit (ECU) are inseparable from it. MCU accounts for about 30% of the total number of autonomous vehicles chips. A car needs dozens to hundreds of MCU chips [52]. Chips shortage prompts many dealers under the temptation of high profits, which makes the market situation of chips shortage become more and more serious.

Chips are a piece that contains integrated circuit components. Chips can be roughly divided into two categories—functional chips (such as CPU and communication base station processing chip) and storage chips. To manufacture a chip, the industry first needs to design the chip. Which function that the chip can have has been determined in the design of this step. This requires professionals to carry out the design of the circuit. Next is the production. This step is also the most tedious. Finally, it is the packaging. The finished chip is mounted into a saleable product. Among these three steps, the most difficult is the design, while the easiest is the packaging.

The chip crisis is bringing a change to reshape the autonomous vehicles business model. The "0 inventory" management can no longer adapt to the development. The pursuit of supply chain costs brings a greater risk of hidden danger. Secondly, cooperation with chip foundries needs to be more open. Previously, vehicle chip foundries were relatively closed. They were unable to respond unexpected situations flexibly. How to cooperate with more chip suppliers on an equal footing in the future is a problem that must be faced. Finally, autonomous vehicle chips need to follow the chip industry upgrade. Now, the vehicles chip is mainly used in 8-inch production line. However, from a technical point of view, 8 inches compared with 12 inches is "past tense"; from the economic efficiency point of view, the 12-inch production line is more efficient than the 8-inch production line.

Autonomous vehicles driving also makes the E–E architecture of vehicles a new trend. The EE architecture of vehicles refers to the layout of the electrical and electronic systems in the vehicle scheme. This concept was first proposed by Delphi in 2007. The electrical architecture is dedicated to providing integrated electrical system layout solutions for automakers to address the development of electrification of vehicles. The original EE architecture was a distributed architecture. Each controller targets one function, which makes add an additional function simple and quick by adding a controller. In the stage of mechanical vehicles, because there are not many electrical and electronic components, the distributed architecture is still comfortable to use. As the level of automotive intelligence increased, distributed architecture began to be caught off guard by the explosive growth of electrical and electronic components.

#### *Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles DOI: http://dx.doi.org/10.5772/intechopen.107486*

Today's autonomous vehicles do not only need abundant acceleration, but also need to be able to see and hear all directions and realize human-machine interaction at any time. With so many functions to achieve, the number of ECUs has increased dramatically; the complexity of the wiring harness in the car has risen; and the maintenance of electrical equipment has become cumbersome to update. How to ensure these functions do not become "congested" in the process of use, and how to call different ECU functions at the same time to react quickly when dealing with complex instructions? The wave of vehicle electrification makes these problems even more difficult.

The new architecture of EE-Centralized Architecture has emerged. It divides the whole vehicle into several domains according to the functions of electronic components (such as powertrain domain, vehicle safety domain, intelligent cabin domain, and intelligent driving domain). Then, a domain controller (DCU) with better processing power is used to unify the control of each domain. The emergence of a centralized architecture has led to a significant increase in the integration of vehicle functions. The role of individual ECU is integrated by integrated management. Complex data processing and control functions are unified in the DCU. With the signals received and analyzed become more complex, the EE architecture will also evolve to multi-domain controller MDC. Because of the new trend of automotive EE architecture, the integration of controllers and the introduction of domain controllers will affect the chip market simultaneously. DCUs, ECUs, and other electronic devices will also be further developed.

The centralized EE architecture also puts new demands on the automotive software architecture. With the centralization of automotive EE architecture, domain controllers and central computing platform deploy in a hierarchical or serviceoriented architecture. The number of ECUs is significantly reduced. The underlying hardware platform needs to provide more powerful arithmetic support. The software is no longer based on a fixed hardware development, but to have portable, iterative, and scalable features. Therefore, at the level of software architecture, automotive software architecture is also gradually upgraded from Signal-Oriented Architecture to Service-Oriented Architecture to better achieve hardware and software decoupling and rapid software iteration.

#### **4.8 Navigation**

In the future, more advanced driver assistance features that can be synchronized with navigation and GPS systems will be developed. Each situation's data that a selfdriving car might encounter will be collected and accumulated. Mapping companies need to enhance 3D mapping data of cities. Automakers and high-tech automotive system suppliers need to work closely with each other to ensure that light detectors, LIDAR, radar sensors, GPS, and cameras work together.

#### **4.9 Iteration**

There are currently two types of iterations: evolutionary and revolutionary. The evolutionary route enables accumulation of more perception and driving data (such as scenario road conditions). These data (such as calibrated image data and corner case driving road conditions) and decision algorithms can be migrated to Level 4 intelligent autonomous vehicles. Revolutionary aims to develop fully autonomous vehicles directly. It remains unclear which path alone will be successful. However, the more likely outcome is a symbiotic fusion of the two.

#### **5. How intelligent autonomous vehicles affect the future**

The automobile was invented in 1886. More than 100 years after the invention of the automobile, the human social life has been changed. Life, living, working, and entertainment have changed dramatically. Therefore, autonomous vehicles may change human life living once again in the following areas.

The value chain of the automobile industry will be reconstructed with the development of intelligent autonomous vehicles. Nowadays, vehicles are mainly hardware devices, and safety is the core. However, the core of the vehicles is changing from hardware equipment to IT services, including autonomous driving system and vehicle networking system. In addition, automobile insurance is being changed. In the future, insurance for human driving may be not needed. Without the needs on insurance for human driving, the insurance companies need to seek new business. Insurance for autonomous vehicles, such as network security and system failure, may become a new trend.

Because of the installation of cameras, radar, LIDAR, and artificial intelligence systems, the cost of autonomous vehicles will be high. This means intelligent autonomous vehicles will be more likely to be firstly used for specific traffic industries and groups [53]. The senior and disabled people may be first group people that use intelligent autonomous vehicles. Intelligent autonomous vehicles give them the freedom to travel without relying on friends and family. Car-hailing, buses, cabs, logistics vehicles may be the first group services that adopt autonomous vehicles. This will significantly reduce traffic congestion and environmental degradation. Because of the changing public transportation [54], the offline shopping mall will be subverted too. From small street stores to the large shopping malls, there are usually large parking lots underneath the malls. However, in the future, after the popularity of self-driving, the vacancy time of the car almost does not exist, and people travel and shopping simply do not need to consider whether there is a large parking lot near the mall. This will have a direct impact on shopping malls.

#### **6. Barriers in intelligent autonomous vehicles development**

In the future, it is inevitable that autonomous driving may replace human driving in most cases. However, new technologies have not been tested over a large amount of time and are not guaranteed to be free of pitfalls. These pitfalls mainly include reliability, legal, and ethical issues. The two pitfalls may barrier the development of intelligent autonomous vehicles.

Reliability of intelligent autonomous vehicles is defined as the ability to perform a given function without failure for a given period of time and under certain conditions. Failures can come from various directions such as control system failures and being hacked. Generally, the more complex the system is that the more automated and intelligent it is, the greater risk of potential stability it will meet. For example, in Arizona, USA, a self-driving road test vehicle struck and killed a cyclist. There was a person behind the wheel of the autonomous vehicles at the time of the accident, but that person was not actually steering the vehicle. These types of accidents reduce public confidence in the ability of autonomous vehicles.

Ethical and legal are one of the most serious issues that autonomous vehicles need to solve if they want to go forward in the future, especially involving the definition of responsibility for accidents. It includes a few of sub-questions. Firstly, the question is

#### *Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles DOI: http://dx.doi.org/10.5772/intechopen.107486*

related to whether an autonomous driving system can be a legally recognized driver or not? The function of driving a vehicle has historically been assigned to a licensed human driver. This determination will have a direct impact on whether self-driving cars can be considered as a legal driver. Secondly, the issue is related to liability. If autonomous vehicles malfunction and cause damage to the owner, whether the owner can seek compensation from the insurance company, and how liability will be allocated between the human driver and the manufacturer in an accident. Legislation on intelligent autonomous vehicles depends on the simultaneous development of technology, regulations, and public consensus building. Thirdly, the issue is related to ethics. In times of emergency condition, whether self-driving cars protect the people inside the car or the people outside the car? Traditional cars are centered on protecting the drivers and passengers for it is decided by human drivers. But self-driving cars can face such ethical controversies because they can be prEEngineered. Moreover, the ethical issues related to the condition that when facing the necessity of hitting one of the two persons in certain situations, who should be the victim? Due to Moore's law, self-driving technology will continue to mature and safety. The legal and ethical issues will become the biggest obstacle for the development of intelligent autonomous vehicles.

#### **7. Conclusion**

Under the impetus of automated driving, artificial intelligence, and other technologies, the major reform of automobile industry has become an inevitable trend. The integration of information communication and automobile industry has become an inevitable move. As an important product reflecting the country's industrial strength, intelligent autonomous vehicles are a new representative of the potential growth of the national economy. Also, it is a manifestation of the integration of the country's industrial manufacturing sector with new technologies. At present, the development space of intelligent autonomous vehicles is getting bigger and bigger, and with the penetration of this type of vehicles, the proportion of autonomous vehicles in the future automobile market will continue to increase.

The chapter reviews the autonomous driving levels and the development of autonomous vehicles first. Then, the chapter explains why autonomous vehicles have huge potential, key technologies that are needed for future intelligent autonomous vehicles, and how intelligent autonomous vehicles affect the future. Finally, barriers in intelligent autonomous vehicles development are discussed. This chapter contributes as a start point for people who want to keep working on intelligent autonomous vehicles and help them understand the general condition of future intelligent autonomous vehicles.

*The Dynamics of Vehicles – Basics, Simulation and Autonomous Systems*

### **Author details**

Yuan Yin Imperial College London, London, UK

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles DOI: http://dx.doi.org/10.5772/intechopen.107486*

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### **Chapter 4**

## A Comprehensive Review on Hybrid and Electric Vehicle Energy Control and Management Strategies

*Hailu Abebe Debella, Samson Mekbib Atnaw and Venkata Ramayya Ancha*

#### **Abstract**

We show a new technology to manage solid waste services through optimization methods (on sectoring, routing costs, and resources). This technology is called optimized planning and integrated logistics management (OPILM). It is being applied to Brazilian municipalities as it attends to their major natural features. The technology is formed by a framework of computational systems that uses optimization methods from sector arc routing and scheduling, fleet and staff scheduling, using also mobile smartphone apps. We present some of the results of real cases evaluated for residential refuse collection and selective waste collection in two Brazilian cities (Petrópolis/RJ and Bom Jesus dos Perdões/SP). The plan implementations achieved 17.9% from actual fixed and variable cost savings for sectors (vehicles and workers) and routes (time and distances) for residential refuse collection in Petrópolis/RJ. For the selective waste collection, we detail how we made our project to Bom Jesus dos Perdões/SP. We also present the returns considering costs involved in the management of the operational level and amortized by the investment required to use and apply the proposed technology for Petrópolis/SP.

**Keywords:** regenerative braking, energy management and control, driving cycles, hybrid electric vehicles, thermal management system

#### **1. Introduction**

The continuing falloff of fossil fuel reserves, in addition to strict emission rules around the world, has made even more critical the need for improved vehicular fuel economy [1–5]. Due to the worsening effect of climate greenhouse gasses and the limited fossil fuels, electric and hybrid electric vehicles (EHEVs) are carrying large responsibilities to freeze the drawback of defective environment and energy

degradation in the recent years. Power management and control strategy is important innovation to boost the EHEV performances. Regeneration of electric energy from braking is the foremost important features of hybrid and electric vehicles. During braking, the motor can function as a generator to recover the vehicle's kinetic energy and potential energy to convert it into electrical energy, which can then be restored to the energy storage devices. Meanwhile, the motor will be controlled and will provide sufficient torque for braking.

Some research on the regenerative brake control approach has been published in the literature, although most of it is limited to flat driving cycles. Despite this, domestic and international researchers have done minimal research on downhill regenerative braking control. The ability to downsize the original internal combustion engine while fulfilling the power demand at the wheels in the case of HEV is the most popular of these benefits. This benefit is due to regenerative braking's capacity to transfer power to recharge the battery from both the internal combustion engine and the electric motor at the same time, resulting in lower fuel consumption and greater driving range [6–10]. The use of an electric drivetrain in a HEV allows kinetic braking energy to be recovered that would otherwise be lost owing to mechanical brakes in conventional vehicles. Accomplishing the aforementioned advantages in a real-time control method by coordinating the onboard power sources, it is a key to enhancing fuel efficiency and minimizing emissions. Various energy controls and management solutions have been advocated in the literature up to this point. This presentation presents a complete analysis of the literature, with a focus on offerings in the area of energy efficiency improvement control and management for electric and hybrid electric vehicles. Available research gaps and future works are also mentioned in this section of the discussion. The following is a list of the offerings in this chapter: First, strategies for HEV modeling, control, and management are briefly addressed in order to emphasize the relative relevance of each approach. Following that, two levels of HEV management and control strategies are examined in depth: HEV offline control strategies and HEV online control strategies. This in-depth analysis focuses on the control structure of the techniques under consideration, as well as their novelty and contributions to the achievement of several improvement goals, including but not limited to: reduced fuel consumption and emissions, charge sustenance, optimization of braking energy regeneration, and improved vehicle drivability. Finally, exploitable research gaps are identified and discussed within the research domain.

#### **1.1 Approaches to HEV Modeling**

In the current development of HEVs, there are at least three stages of computational modeling. The following are the stages:

*A Comprehensive Review on Hybrid and Electric Vehicle Energy Control and Management… DOI: http://dx.doi.org/10.5772/intechopen.111421*

• Hardware-in-the loop (HIL) modeling is done after the controllers have been manufactured and validated. At the detailed modeling stage of the development process, there are three common ways for HEV modeling: the kinematic or backward approach, the quasi-static or forward approach, and the dynamic approach [11].

#### **1.2 Kinematic approach**

The kinematic approach, as depicted in **Figure 1**, is a backward methodology in which the input variables are the vehicle's speed and the road's slope angle. This method calculates the engine speed using basic kinematic connections based on the wheel revolution speed and the total transmission ratio of the driveline. From the major vehicle characteristics, the tractive torque that needs to be applied to the wheels to drive the vehicle according to the selected speed range may be computed (e.g., vehicle mass, aerodynamic drag and rolling resistance).

To obtain an immediate fuel consumption or emissions rate estimate, the calculated engine torque and speed are combined with a statistical fuel consumption model [12]. The kinematic technique implies that the vehicle matches the goal performance; hence, the vehicle speed is purportedly known a priori [13]. As a result, the kinematic approach has the benefit of simplicity and cheap computational cost. The kinematic modeling method, also known as backward modeling, ensures that the driving speed profile is followed exactly. There are no guarantees that the given vehicle will be able to complete the specified speed trace because the power needed is immediately generated from the speed and not checked against the actual powertrain capabilities. A "fail-safe" component is usually included in the kinematic approach, which terminates the simulation if the required torque exceeds the maximum torque available (from the electric motor and engine). Another issue in this modeling technique is that it ignores engine thermal transient activity, which is seen after a cold start. Because transient situations are simplified into a series of stationary states, this modeling method is only useful for estimating vehicle fuel consumption and emissions in the first stages [11].

#### **1.3 Quasi-static approach**

**Figure 2** depicts, the quasi-static technique of HEV modeling uses a driver model, usually a PID, which compares the desired speed of the vehicle (driving cycle speed) with the vehicle's real speed profile and then generates the power demand profile necessary to meet the target speed of the vehicle profile. The vehicle's differential motion equation is used to construct this profile of power demand [14]. After determining the engine's propulsion torque and speed, instantaneous consumption of fuel can be calculated using a statistical engine model, as stated in the kinematic or backward approach. The applicability and precision of the quasi-static modeling

**Figure 1.**

*Information flow in a kinematic or backward HEV model [11].*

**Figure 2.** *Information flow in a quasi-static powertrain model [7, 11].*

approach are highly dependent on the type of simulation models to be performed. When it comes to estimating the consumption of fuel and NOx of a car with a conventional powertrain, the quasi-static modeling approach delivers reasonable accuracy. Acceleration transients and related "turbo-lag" phenomena make a significant contribution to cycle cumulative emissions for pollutants like soot, demanding a more complex engine simulation model capable of adequately representing engine transient behavior [13].

#### **1.4 Dynamic modeling approach**

The internal combustion engine behavior during transient conditions is also taken into account in the dynamic modeling technique, in addition to longitudinal vehicle dynamics. The engine's transient behavior is represented by a complete onedimensional fluid dynamic model. In a dynamic modeling technique, an internal combustion engine engine's suction and discharge systems, for instance, are represented as a network of tubes connected by connections that reflect either physical joints between the tubes, such as area changes or volumes, or subsystems, namely the engine cylinder. A finite difference technique can then be used to find solutions to the equations governing mass, momentum, and energy flow for each element of the network. This enables highly dynamic events, such as rapid vehicle accelerations, to be correctly simulated. Dynamic modeling is often restricted to study areas requiring internal combustion engine development because it involves a substantial amount of time and calculation [15, 16]. The quasi-static technique is preferred for control development because it preserves the physical causality of the vehicle system and enables the use of the identical controller inputs/outputs in the simulator as well as on the real vehicle.

#### **2. Power split configurations and HEV modes**

HEVs have been shown to improve automotive fuel efficiency and emissions while meeting vehicle power demands, preserving vehicle performance, and offering a better driving experience [12]. Regardless of the HEV configuration in question, effective power distribution across the energy sources is required to achieve improved fuel efficiency and reduced emissions (ICE and electric motor). As part of this endeavor, several power split control systems have been suggested, evaluated, and

#### *A Comprehensive Review on Hybrid and Electric Vehicle Energy Control and Management… DOI: http://dx.doi.org/10.5772/intechopen.111421*

applied to various HEV configurations. HEVs' power-split controller gets inputs from the global positioning system (GPS), including vehicle power demand, vehicle speed or acceleration, battery state of charge, current road load, and, on occasion, "intelligent" future traffic circumstances. A set of control decisions are contained in the controller outputs signal, which governs which of the following modes the HEV should operate in:

#### **2.1 HEV management and control strategies**

Minimization of fuel consumption and emissions without compromising of vehicle performance, and battery state of charge are often the main control objectives of most HEV control strategies. As illustrated in the control strategy classification chart in **Figure 3**, HEV control techniques may be divided into two categories: online control strategies and offline control strategies. Although various papers and research publications have contributed to the compilation of reviews on HEV control tactics, this field of research is always expanding, and there is a need for an up-to-date review with the introduction of novel methodologies. This section's major goal is to highlight important research gaps in the field as well as contribute to the increasing list of review debates. On the basis of **Figure 3**, all HEV management and control solutions were discussed.

#### *2.1.1 HEV offline energy management strategies*

Control signals are determined using optimization-based control algorithms that minimize the sum of the goal function over time (global optimization) or minimize the objective function instantly (instantaneous optimization) (local optimization). Because the effectiveness of a global optimal control technique is solely dependent on a priori knowledge of the entire driving cycle, which is often difficult to determine in real time, global optimal techniques are often referred to as "non-causal," meaning they cannot be used in real time but can be used as a control benchmark against which all other causal real-time controllers can be compared. For optimal energy management of HEVs, global optimization approaches such as linear programming, dynamic programming, genetic algorithms, and others have been used.

#### *2.1.2 Linear programming*

The nonlinear fuel consumption model of an HEV is approximated and solved for a global optimal solution using linear programming [11]. Automotive energy management challenges have been effectively solved using linear programming.

**Figure 3.** *HEV control strategy classification redrawn from [12].*

#### *2.1.3 Dynamic programming*

The dynamic programming technique was invented by Richard Bellman and is used to determine optimal control policies through a multi-stage decision process. An ideal control policy, as described by Bellman, has the property that no matter what the prior decision (i.e., controls) was, the remaining decisions must comprise an optimal policy in terms of the state resulting from those previous decisions [17]. A discrete multi-stage optimization problem in which a decision based on the optimization criterion is taken from a finite set of decision variables at each time step is known as a dynamic programming algorithm. The backward recursive method and the forward dynamic programming methodology can both be used to implement Bellman's dynamic programming algorithm. The best sequence of control variables is found by working backward from the final state and selecting the path that minimizes

#### *A Comprehensive Review on Hybrid and Electric Vehicle Energy Control and Management… DOI: http://dx.doi.org/10.5772/intechopen.111421*

the cost-to-go at each time step in the backward recursive technique (integral cost from that time step until the final state).

#### *2.1.4 Genetic algorithm*

The genetic algorithm (GA) is a heuristic search algorithm for developing optimization solutions. Darwin's theory of evolution inspired this branch of artificial intelligence. GA starts with a population of preliminary solutions (chromosomes) to find the best solution to a problem. Each population's solutions are picked based on their suitability for forming new and enhanced versions. As a result, the most suited solutions have a better chance of growing than those that are less suitable. The method is performed indefinitely until the desired optimization conditions are met. The genetic algorithm is a reliable and practical global optimization method with a large search space that may be used to solve complex engineering optimization problems with nonlinear, multimodal, and non-convex objective functions.

#### *2.1.5 Particle swarm optimization*

Dr. Eberhart and Dr. Kennedy created particle swarm optimization (PSO) in 1995 [18, 19]. This method is based on the social behavior of flocking birds, which optimizes a problem by iteratively trying to enhance a candidate solution in terms of a specific quality metric. Particles in PSO travel about a search space, directed by the search space's best known positions as well as the swarm's best known position. When better sites are located, the swarm particles will move. PSO is a meta-heuristic method for searching a large number of candidate solutions. PSO does not require the optimization problem to be differentiable, despite its non-causal character, and is thus well suited to optimization problems with some degree of noise or irregularity. In HEVs, particle swarm optimization has proven to be effective.

#### **2.2 HEV online energy management strategies**

HEV online control strategies, in contrast to HEV offline control strategies, are causal and implementable in real time. Heuristic control rules (rule-based control strategies) or an instantaneous optimization of a stated objective function can be used to create HEV online control techniques (online optimization-based strategies).

#### *2.2.1 Rule-based energy management strategies*

The most typical method of establishing real-time supervisory control in a HEV is using a rule-based control scheme. The control rules are frequently based on heuristics, engineering intelligence, or mathematical models, and they are designed to allow the ICE to function at high-efficiency levels while still allowing for energy recovery via regenerative braking. The development of rule-based HEV control approaches is typically divided into two stages: the formulation of appropriate powertrain control rules and the calibration of the strategy, which is commonly accomplished through simulations on a vehicle model. In most cases, rule-based control approaches fail to guarantee the optimality of the solution or to fulfill the required final integral constraint (charge sustainability). To correct this, the control rules must ensure that the

integral constraint state of charge (SOC) remains between the lower and upper boundaries as specified. There is no standard approach to forming control rules in rule-based control strategies, and there is no way to know a priori whether a certain set of rules is adequate for a given application. However, the control rules could be designed deep and complex enough to handle any exceptional event that might occur [20].

The fundamental benefit of rule-based HEV control approaches is their simplicity, which makes them very simple to comprehend and execute on actual cars [11]. Rulebased control techniques have monopolized the production vehicle industry due to their minimal computational demand, natural adaptability to online-applications, high dependability, and reasonable fuel consumption outcomes. Despite their extensive use, rule-based HEV management systems still face considerable obstacles. Due to the lengthy rule definition and calibration process, developing a rule-based HEV management approach typically necessitates a significant amount of time and investment in competent labor. This scenario is exacerbated by the necessity to change the rules for each new driving circumstance and engine, raising concerns about the robustness of rule-based HEV control schemes. Furthermore, current research investigations reveal that rule-based HEV control approaches offer lower but acceptable fuel consumption results when compared to optimization methods. Deterministic rule-based control strategy and fuzzy rule-based control strategy are two types of rule-based controllers [11].

#### *2.2.2 Deterministic rule-based control strategy*

The rules for the deterministic rule-based control method are determined using the engine's fuel economy or emissions map. The rules are frequently implemented using pre-computed look-up tables. The concept of a hybrid optimum line for a parallel HEV with continuously variable transmission (CVT) was introduced by Kim et al. [21], who used a deterministic rule-based control technique. The best values of CVT gear ratio, motor torque, and engine throttle were effectively determined and applied in real time using this method. The electric assist control approach is one of the most widely used deterministic rule-based HEV control strategies. The ICE is employed as the sole source of power in this technique, and the electric motor is only used to provide additional power when the vehicle requires it. Another type of deterministic rule-based control is thermostat control technique. The electric motor and ICE are employed to generate the electrical energy that powers the car in this manner. By simply turning on/off the internal combustion engine, the battery state of charge is always maintained between specified high and low values. The thermostatic control technique was employed by Jalil et al. [22] to turn the engine on/off based on the battery state of charge profile. When compared to a deterministic rule-based control technique, the obtained outcomes were shown to be considerably sub-optimal. The following rules apply to several of the most extensively used rule-based control systems [13]:

1.When the vehicle's power demand falls below a specified threshold, the vehicle operates solely as an electric vehicle (EV), with the electric motor supplying the whole power requirement. This rule is usually applied to prevent the engine from operating at low-efficiency levels. The applicability of this rule, however, is contingent on the size of the HEV's electric engine and batteries.

#### *2.2.3 Fuzzy rule-based control strategy*

Rule-based controllers are the forerunners of fuzzy rule controllers. In the fuzzification and defuzzification process, the linguistic representation of the control inputs is turned into a numerical representation with membership function in a fuzzy rule controller. The fuzzy rule-based control strategy's underlying logic is a type of multivalued logic derived from fuzzy set theory, which is designed to deal with reasoning that is approximate rather than precise. The relative simplicity of fuzzy rule controllers allows for tuning and adaption as needed, hence increasing the degree of control flexibility. Because of its nonlinear structure, it is very useful in complicated systems like modern powertrains. The battery level of charge, desired ICE torque, and intended mode are often inputs to fuzzy rule controllers, which output the ICE operating point. Driver command, battery SOC, and motor/generator speeds were treated as fuzzy sets for the creation of a fuzzy rule-based control strategy considered by [23, 24]. A power notification system was added to the fuzzy control framework, allowing the engine to operate in its high-efficiency region. In most cases, the electric motor compensates for the gap in power demand and ICE power. Traditional fuzzy control strategy, adaptive fuzzy control strategy, and predictive fuzzy control strategy are some of the current varieties of fuzzy rule-based control [13].

#### *2.2.4 Traditional fuzzy control strategy*

Traditional fuzzy control is frequently used to improve fuzzy efficiency, allowing the ICE to run more efficiently. This is accomplished by load balancing, which involves using the electric motor to push the engine to operate in its most efficient region (low engine speed, high engine torque) while maintaining the battery charge level. In a parallel HEV, Sulaiman et al. [25] presented a fuzzy logic controller to optimize fuel usage. The proposed strategy is centered on improving the efficiency of the vehicle's most important components, such as the internal combustion engine, electric motor, and battery.

#### *2.2.5 Adaptive fuzzy control strategy*

Fuzzy control that adapts because it potentiates simultaneous optimization of fuel efficiency and emissions, strategy is becoming increasingly popular in automotive applications on HEV. Because fuel efficiency and emissions are frequently at odds, an optimal solution cannot be found that meets all of the goals. However, utilizing the weighted-sum approach, where appropriate weights are modified over different driving situations for fuel efficiency and emissions, a sub-optimal solution is possible. The weights are relative, reflecting the importance of the particular objectives (fuel usage, NOx, CO, and HC emissions) [13]. As a result, adaptive fuzzy controllers can control specific objectives by modifying the weights given to them.

#### *2.2.6 Predictive fuzzy control strategy*

Prior information about a planned driving trip is used by a predictive fuzzy controller. This information is frequently obtained *via* a global positioning system (GPS), which provides information on the kind of impediments that the vehicle is likely to encounter, such as heavy traffic and a steep incline, among other things. Vehicle speed, speed state in the look-ahead window, and elevation of sampled places along a specified route are all common inputs to the predictive controller. The predictive fuzzy controller calculates the optimal ICE torque contribution for each vehicle speed based on the available history of vehicle motion and the speculation of its possible future motion, and outputs a normalized signal in the order of 1 to +1, indicating whether the battery should be charged or discharged. Fuzzy controllers have drawn a lot of interest from heuristic control professionals in the research and automobile industries due to their simplicity and robustness.

#### **2.3 Online optimization-based strategies**

Online optimization-based solutions break down global optimization problems into a series of smaller problems, lowering the amount of time and effort required to solve them. This eliminates the requirement for future driving data, allowing for realtime implementation. Local optimization procedures have gained the most research interest in HEV management, although producing marginally sub-optimal results when compared to global optimization strategies. The most widely used strategies are ECMS (Equivalent Consumption Minimization Strategy) [26] and PMP (Pontryagin's Minimum Principle) [3, 21]. Artificial neural networks, particle swarm optimization (PSO), and model predictive control (MPC) are some of the other online optimization-based methodologies now being investigated.

#### *2.3.1 Pontryagin's minimum principle*

Pontryagin's minimal principle (PMP) is a specific instance of the Euler-Lagrange equation of the calculus of variations, which was formulated in 1956 by Russian mathematician Lev Pontryagin and his students. The optimal solution to the global optimization issue must satisfy the criterion of optimality, according to the principle. The performance of the PMP controller was shown to be particularly sensitive to the estimated co-state value after studying the state of energy evolution for various costate values. The model-based PMP control technique was discovered to compel the vehicle to deplete the battery for a co-state value larger than 10, and then transition to a charge-sustaining mode when the lower SOE (State of Energy) bound is achieved. Likewise, when the co-state value is 6, the model-based PMP technique allows the battery to be progressively reduced during the cycle, reaching the lower SOE boundary only at the end of the driving pattern and avoiding any charge maintaining activities. This process, known as blended mode, enables the achievement of the lowest possible vehicle fuel usage throughout a set of driving cycles. According to the findings of Stockar et al. [27], PMP is a shooting approach for solving a boundary

#### *A Comprehensive Review on Hybrid and Electric Vehicle Energy Control and Management… DOI: http://dx.doi.org/10.5772/intechopen.111421*

value optimization issue. As a result, the optimal control technique that emerges is non-causal and cannot be executed in real time.