Figure 2 shows the composite component in the AASX package explorer of a line set section. The left side of the figure represents the Industry 4.0 component, which consists of the asset (gray) and the AAS (blue). The lower left third depicts the repository, through which the AASs of the individual components of the composite component can be selected. The middle third shows a hierarchical structure from the AAS, through which various sub-models (SM) to the sub-model element (carries the respective information) are shown. The right third shows the information stored in the sub-model element and much more. In this case the hierarchical structure of a Bill-of-Material (BOM) can be seen there. The composite component shown here consists of five individual components (tape, cable, clip, connector, and flat connector housing), which all have an entity. The individual entities are then related to each other by the "Relationship Element" sub-model element.

**Fig. 2.** AAS of a composite component

# **5 Conclusion and Further Development**

The VWS4LS project has successfully completed the first steps, though there is more to come. New use cases have become possible. The creation of the complete wiring harness will enable a continuous access to all the data of the related supply chain. Based on the complete information, the system engineering will be significantly more efficient, especially given that they may even use automated processes. Integrated information, such as the carbon footprint of the complete wiring harness assets, including manufacturing can be determined. The data will be available over the complete life cycle allowing use cases, such as the components' re-use and recycling (Circular economy).

In the future, numerous further technical topics need to be addressed. One important aspect is the automated merging of AAS. The wiring harness consists of numerous individual components from different suppliers that are combined to form an assembly. Today, this is done with a great deal of manual effort. Within the project, the possibilities for automated production are to be investigated. An important aspect is change management; some questions that would arise herein include, but are not limited to the following: What happens to data when components change? And how can traceability still be guaranteed?

A particularly important topic in VWS4LS is the monetisation of data. Within the framework of the project, considerations on new business models and their implementations with the AAS must be developed. In this context, a data storage policy needs to be developed that defines who has access to what data and under what conditions. Another important aspect is automated negotiation processes. Here, negotiation scenarios and strategies need to be explored.

A very important aspect that is not covered in VWS4LS is how the data gets into the AAS. Since the new generation of AAS is currently still a manual process supported by tools, we see a great benefit by semi-automating the generation of assets by using semantic interoperability based on a neural language model [7].

**Acknowledgement.** The authors gratefully acknowledge the support from The German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the VWS4LS project (Grant No.13IK005A). Further thanks go to the many active participants in the project and the working groups of the Industry 4.0 platform and related initiatives.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Case Study on Localization for Robotic Wire Harness Installation**

Markus Wnuk1(B) , Manuel Z¨urn1, Matthias Paukner2, Sascha Ulbrich2, Armin Lechler1, and Alexander Verl1

> 1 University of Stuttgart, 70174 Stuttgart, BW, Germany<EMAIL_ADDRESS>2 KUKA Systems GmbH, Augsburg, Germany

**Abstract.** Wire harness installation is one of the most challenging processing steps for automation in automotive production. Wire harnesses have an infinite number of degrees of freedom, such that they change their shape continuously during manipulation. As of today, the human's ability to perceive the wire harness and deal with its shape changes, is unmatched by any technical solution. Therefore, wire harnesses are still installed manually. This paper proposes a concept for wire harness localization and applies it for robotic wire harness installation. The concept uses two stereo cameras, one to perceive the shape of the wire harness and obtain rough position estimates of wire harness components, and a second for accurate 6D pose estimation of the individual components. The concept is evaluated with a case study on localization in a car chassis, where the accuracy and limitations of the concept are investigated by practical experiments.

**Keywords:** wire harness *·* localization *·* robot *·* installation

# **1 Introduction**

Wire harness installation is a very challenging task. Especially in automotive production, where its size and complexity make it one of the heaviest and most expensive components [1]. Therefore, wire harnesses are mostly installed manually, which is a physically demanding task [2]. Compared to wire harness assembly, where several processes have been automated in the last decades, wire harness installation in the car remains to have a low level of automation [3]. However, automation offers the potential to increase process reliability, reduce physical load on workers, and decrease costs in the long term. Therefore, this paper aims in the direction of achieving higher automation in the process of wire harnesses installation in the car chassis, as shown in Fig. 1.

# **2 Problem Statement**

In research, wire harnesses are referred to as semi-deformable linear objects (SDLOs) [4]. They consist of rigid components such as clips, plugs, wire channels,

**Fig. 1.** Installing a wire harness in a car chassis. The proposed localization concept is used to determine grasp poses for the robot.

and fixations interconnected by deformable parts [1]. Typical processing steps of wire harness installation include routing the wires, inserting the plugs into their mating parts, mounting clips, and connecting any wire ends to appropriate terminals [5]. Wire harness perception for the installation process can be split into:

# **3 Related Works**

Relevant works that address wire harness localization can be divided into two categories. Approaches from the first category, such as [7] and [8], aim at localizing the deformable parts of the wire harness. They use a series of segmentation and estimation methods to distinguish individual wires, even in entangled wire harness assemblies. However, these works consider aircraft wire harnesses and mainly address applications in aeronautics, such as wire defect detection. Therefore, the focus of these approaches lies on visual characteristics such as distances between wires, colors, or bending, while not considering the circumstances given in automotive wire harness installation. One of the most comprehensive works on the use case of automotive wire harness installation is presented in [9]. They address the problem of perception by attaching visual markers to the wire harness and its components. This only yields a partial representation of the wire harness, at locations, where markers are attached and visible to the camera. Hence, there is no information about the mechanical coupling between the detected markers available. This is a limiting factor for certain installation steps.

Approaches from the second category neglect the deformable parts and only aim to localize individual components. In [4], the authors investigate the process of mating different electrical plugs of the wire harness. For localization, they use a global camera for detecting the component in the workspace using a deep learning neuronal network, trained on labeled images of the components. For accurate 6D pose estimation, they use a 2D in-hand camera system with template matching of 2D shape representatives obtained from 3D CAD data of the plugs. The approach is similar to the proposed concept in the sense that it uses two camera systems. However, it can only estimate components, but not the shape and configuration of the wire harness. Another approach from this category is presented by [10]. They use color heuristics to estimate the positions of plugs from 3D RGB-D data using traditional image processing methods. The focus of their method is position estimation and therefore, they do not obtain the 6D pose of the components. However, for certain automotive wire harness installation tasks, the orientation is crucial, especially when subsequent mating requires a specific orientation.

# **4 Contribution**

Despite previous approaches have made great efforts to solve the challenge of localization, none of the investigated methods can fully fulfill the requirements for automotive wire harness installation. Therefore, this paper proposes a twostep localization concept. The concept estimates the current shape of the wire harness first. Hereby, we are capable of finding and distinguishing geometrical identical components and also estimate their mechanical coupling. In a second step, the pose of each component is obtained with high accuracy from a narrow field of view, by state-of-the-art template matching using the components' known geometric model. The presented concept is evaluated with a case study on clip installation of a reference wire harness with a seven-axis lightweight robot in car chassis.

# **5 Concept**

The proposed concept uses two stereo cameras with different fields of view for the localization, see Fig. 2. The first camera is used to find the shape of the wire harness. Therefore, it is referred to as "**C**amera for **W**ire harness **L**ocalization" (CWL). The idea is to estimate a narrow region of interest for the second camera, which is referred to as "**C**amera for **C**omponent **L**ocalization" (CCL). The CCL is mounted on the robot end-effector to allow flexible repositioning. Eventually,

**Fig. 2.** Schematic of the test bench setup. Depicted is the concept of different fields of view of the CCL and CWL. The specific component of interest for accurate pose estimation is highlighted in purple.

the goal is to obtain an accurate estimation of the position and orientation of individual components, which are illustrated as target points in the figure.

The concept consists of two steps. First, the shape of the wire harness is determined. This allows for rough position estimations of all wire harness components. The estimated positions can then be used to distinguish individual components by comparing the estimated position of the component with their respective position on the wire harness. Note that this also allows estimating mechanical coupling between the components, which determines the available motions during a subsequent manipulation. In the second step, a specific component, chosen from a higher-level assembly logic, can be localized by moving the CCL over the estimated position of the component. This allows a high resolution pose estimation, as the camera operates in a narrow field of view of the component.

### **5.1 Wire Harness Localization**

The CWL creates a point cloud of the entire workspace. The point cloud is preprocessed to segment the wire harness from the environment. Here we use 2D image processing, such as binary filters and background subtraction, together with 3D image processing, such as clustering and box filters for outlier removal.

After preprocessing, the localization is performed by registering a model of the wire harness to the obtained point cloud representation of the wire harness. For this step, we use non-rigid registration methods, such as [11] or [12], to estimate correspondences and match the model to the point cloud. This process is shown in Fig. 3a.

After registering the model to the point cloud, the rough positions of the components can be obtained, as each component is rigidly attached to the wire harness. Therefore, the local position of a component on the wire harness encodes its position uniquely in the workspace.

(a) Wire harness localization by nonrigid registration.

(b) Component localization for a clip by template matching.

**Fig. 3.** Estimation methods for the steps of wire harness and component localization.

## **5.2 Component Localization**

Using the rough positions obtained from the estimated wire harness configuration, the robot can move the CCL, such that its field of view covers the targeted component. The CCL then acquires a high-resolution point cloud of the component. From this point cloud, we can obtain an accurate pose estimation using template matching [13]. Template-matching algorithms are often directly integrated into software libraries of modern stereo vision systems and can be readily used. They yield accurate estimations of the position and orientation given a CAD model of the component. For the system used in this paper, the matching result of a wire harness clip is shown in Fig. 3. From the estimated pose of the component, a suitable grasping pose for the robotic manipulator can be obtained by specifying the gripper geometry and desired grasping points.

# **6 Evaluation**

### **6.1 Experimental Setup**

For the experimental evaluation, a setup according to the concept of Fig. 2 is built. The scenario consists of a robot, two stereo vision camera systems, a car chassis, and an exemplary wire harness. The robot is a *KUKA iiwa* lightweight robot with 7 ◦C of freedom. This allows for high flexibility in the constrained workspace. The CCL is a *Roboception rc visard 65 stereo camera*, which was hand-mounted on the robot end-effector. The CWL is a *Nerian Scarlet* stereo vision system, which is fixed in the workspace on the top of the car chassis. The camera setup is depicted in Fig. 4. A reference harness is used to evaluate the localization concept. Its design was created in consultation with automotive companies associated as partners within the research campus *ARENA2036*. The wire harness consists of one central trunk of 1 m in length and three additional branches of 0.44 m, 0.36 m, and 0.15 m, and a radius of 0.01 m. The wire harness is fixed in the car chassis with custom-made fastening clips, which are used as reference components in the evaluation. They are modified for easier grasping with a robotic gripper, as depicted in Fig. 4b.

(a) Camera for wire harness localiza-

tion. (b) Camera for component localization.

**Fig. 4.** Camera setup for the experimental evaluation of the localization concept.

#### **6.2 Evaluation of Wire Harness Localization**

The evaluation of the wire harness localization aims at investigating if the configuration of the wire harness can be localized by the CWL with sufficient accuracy. Sufficient accuracy is measured by the subsequent localization with the CCL, as the rough clip position is encoded by the wire harness localization of the CWL.

For this purpose, a reference to measure and compare the performance of the localization is defined. This was achieved by fastening the wire harness with four clips in the car chassis. For each of the clips, successful grasping poses are determined. Successful grasping poses are used as a ground truth reference for further evaluation.

Assuming no prior knowledge of the ground truth clip positions, wire harness localization is performed. From the registered model, the positions of the four mounting clips are estimated. The estimated positions are then compared to the ground truth positions. Since it cannot be assumed that the initial configuration of the wire harness is accurately known, the model is initialized in different poses. The initial poses are randomly sampled with a positional uncertainty of *±*10 cm in the mounting plane and a rotational uncertainty of *±*20◦ around the normal of the plane. The localization was performed for nine different configurations.

Exemplary results are shown in Fig. 5. The model is depicted in blue, the observed point cloud in black, the estimated configuration from non-rigid registration is shown in red, and the positions of the mounting clips are indicated by black boxes for the measured ground truth positions and blue for the model's estimates. To quantitatively assess the accuracy of wire harness localization, the error between the measured ground truth and the estimate is defined by the euclidean distance. To assess the accuracy of the wire harness localization, the error is averaged for each clip over the conducted experiments. Figure 6 shows the evaluation scenario with the enumeration of the clips depicted in Fig. 6a, and the measured mean position error for each clip given in Fig. 6b. The position error

**Fig. 5.** Exemplary tracking results for the evaluation of the wire harness localization with different initialization poses of the model.

remains below 5 cm for all experiments. This determines the maximum uncertainty which can be used to define a region of interest for the clip localization. CCL should be positioned such, that an area of *±*5 cm around the estimated clip position is covered, to ensure that the sought clip lies within its field of view.

For all randomly sampled initial configurations of the wire harness the clip error after registration lies below 5 cm, which is a sufficiently accurate estimate for targeting the field of view of the subsequent component localization towards the desired clip. The standard deviation over all experiments remains between *±*0*.*1 cm to *±*2 cm. This can be used to define a confidence measure for distinguishing different clips. Considering a radius of the standard deviation around every clip's estimated position, clips which lie outside the radius can be clearly distinguished and uniquely identified from their estimated positions alone. Within the radius, such a distinction cannot be made, and the clips need to be distinguished differently, for example by unique geometry, color, or texture information. However, in our experiment, all clips could be uniquely identified because they were mounted with a standard deviation greater than 2 cm apart.

#### **6.3 Evaluation of Component Localization**

The evaluation of the component localization aims to investigate if the individual components can be localized with sufficient accuracy for grasping. In the conducted case study the attached clips are considered the components and serve as

(a) Clip enumeration. (b) Accuracy of wire harness localization.

**Fig. 6.** Evaluation of wire harness localization.

a reference for the evaluation. From the prior wire harness localization, a rough estimate of the position for each clip is available. Using this information, the robot is positioned such that the desired clip lies within the field of view of the CCL. However, many poses can be considered for positioning the CCL. Therefore, component localization is performed from different poses in the workspace.

The experiment is performed for a single wire harness clip, where its ground truth pose is determined, as in the previous experiment, by performing a successful grasp on the clip. The clip is localized by the CCL from seven different perspectives, each time rotating the robot end-effector for several degrees. For each pose, the positional and angular errors are determined from comparison with the ground truth. The positional error is measured by the euclidean distance between the estimated position and the ground truth. The angular error is measured as the norm of the Rodrigues angle between the measured orientation and the ground truth. Figure 7 shows the results.

From the results, it can be seen that the positional error remains below 1 mm and the angular error remains below 2 ◦ for six of the seven poses. The first pose yields a larger positional and angular error of up to, 1.82 mm and 15 ◦. From experience, we rate this measurement as an outlier. Therefore, the average positional accuracy is 0.46 mm, and the rotational accuracy is 0.94 ◦.

From the experiments, it can be observed that the measurement errors are in the range of 1 mm for the positional error and 2 ◦ for the angular error. This yields successful grasps, since errors within this range generally can be tolerated by the gripper design. However, outliers such as seen for the first pose, can occur and yield a probability to result in an unsuccessful grasping attempt. A practical approach to counteract such measurement errors might be self-centering clip and gripper geometries which provide positional and rotational tolerances during grasping.

**Fig. 7.** Evaluation of component localization for wire harness clips.

## **7 Conclusion**

This paper presents a concept for wire harness localization. The process is split into two steps. This allows covering a large workspace while maintaining high precision for the pose estimation of individual components.

Within the experimental evaluation of the presented localization concept, clips could be estimated with an average error of 1 cm to 4.3 cm in the car chassis. This was sufficient for positioning the CCL to perform accurate localization with a positional accuracy of 0.46 mm, and a rotational accuracy of 0.94 ◦ in a narrow field of view. Grasping attempts with a positional accuracy of less than 1 mm and rotational accuracy of less than 2 ◦ allowed successful grasps in this case study. From the results, it can be expected that the concept can be generalized for the localization of other components such as plugs or wire channels.

Nonetheless, there remain limitations to the proposed method. First, the wire harness localization needs an entire view of the wire harness and a sufficiently good estimate of the initial configuration to perform successful registration. Second, overlapping of wires needs to be avoided because the non-rigid registration could converge in physically implausible local minima, which causes the localization to fail. Third, the components need to be constrained to specific orientations, as the robot cannot re-grasp and re-position the component arbitrarily.

Future research will be necessary to improve the concept. Methods that are capable to localize the wire harness from partial views and recover from false position estimates or failed grasps will make the process more robust and help to get closer to the goal of automated wire harness installation.

**Acknowledgments.** This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the International Research Training Group "Soft Tissue Robotics" (GRK 2198/1) and Germany's Excellence Strategy - EXC 2075 - 390740016.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Traceability in Battery Production: Cell-Specific Marker-Free Identification of Electrode Segments**

Günther Riexinger1(B) , David J. Regina2, Christoph Haar1, Tobias Schmid-Schirling2, Inga Landwehr1, Michael Seib2, Jonas Lips1, Simon Fehrenbach2, Julian Stübing1, Daniel Carl2, and Alexander Sauer1,3

1 Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstr. 12, 70569 Stuttgart, Germany

<EMAIL_ADDRESS>
2 Fraunhofer Institute for Physical Measurement Techniques IPM, Georges-Köhler-Allee 301, 79110 Freiburg im Breisgau, Germany

3 Institute for Energy Efficiency in Production, University of Stuttgart, Nobelstr, 12, 70569 Stuttgart, Germany

**Abstract.** Digitalization in battery production, as well as the increase and stabilization of product quality of lithium-ion battery cells, require the elimination of information gaps between processes to enable the traceability of components and process steps to the finished product. In lithium-ion battery cell manufacturing, using a traceability system is considered a promising approach to reduce scrap rates and enable more efficient production. Today, traceability is possible from the assembled cell onwards. However, with a view to the new EU battery regulation, complete traceability down to the material needs to be ensured. One of the challenges in this context is to ensure both, traceability in continuous electrode production and cell-specific identification within the production chain. This paper presents an approach for identifying individual electrode segments without identification markers, using the individual microstructure of the electrode surface. The Fraunhofer Institute for Manufacturing Engineering and Automation IPA and the Fraunhofer Institute for Physical Measurement Techniques IPM are further developing and testing an identification technique known as Track & Trace Fingerprint. This technique is dedicated to serialization within battery production as part of the joint project DigiBattPro 4.0 and to be implemented at the Center for Digitalized Battery Cell Manufacturing (ZDB) of Fraunhofer IPA.

**Keywords:** Traceability · Identification · Battery Production · Digitalization · DigiBattPro

# **1 Introduction**

## **1.1 Traceability in Lithium-ion Battery Production**

Traceability not only plays an important role in production but also over the entire life cycle of a battery cell. Thus, the EU Commission anchors transparency along the entire supply and value chain in its proposal for a regulation concerning batteries and waste batteries. In addition to the CO2 footprint during production, information about the origin and composition of the materials, repair or reuse, and end-of-life options such as recovery and recycling processes should also be provided [1].

In lithium-ion battery production, using a traceability system is considered a promising approach to reduce scrap rates, lower costs, and enable more efficient production [2]. It allows identifying possible problems or defects at an early stage of production. A traceability system collects information from trace objects, e.g. a single part or a segment of a continuous product during different phases of the product life cycle or at different production steps. It enables the assignment of process information based on tracing data for these objects. Generally, such systems consist of core elements, such as identification, data acquisition, data linking, and communication, as shown in Fig. 1 [3].

**Fig. 1.** Traceability system overview, adopted from [4].

In industrial battery production, an approach for cell-specific identification of electrode segments has not yet been established. Serialization becomes almost impossible when splitting the coil into subcoils or cutting out additional segments. Initial cellspecific serialization usually takes place after electrode production during cell assembly. Identifying cell-specific electrode segments within the electrode production process is currently one of the biggest challenges in cell production of lithium-ion batteries. The reasons can be summarized as follows:

• Change of batch structure (Raw materials → electrode paste → electrode coating → electrode sheets → cell assembly) [5].

## **1.2 Identification Techniques**

Various techniques were investigated to select suitable identification solutions for lithium-ion battery production. Basically, trace objects are distinguished by their indirect and direct object features.

In the case of identification through indirect object features, the identification data is placed either on the workpiece itself or on workpiece carriers. The applied identifier is detected either contactless or tactile [6]. Optical markings belong to the group of contactless identification techniques. For this purpose, plain text, symbols, or an ndimensional code such as a 1D bar code, a 2D Data Matrix code (DMC) or a color code is applied to the trace object and detected optically via digital image processing. Further options include electromagnetic or radio-based approaches. Trace objects could be equipped with active or passive radio modules (RFID, Bluetooth, WLAN modules), which transmit their identity signals. Tactile techniques require direct contact between identifier and sensor. For example, this includes devices that read the integrated circuits of smart cards.

Identification by direct object features eliminates the need for new identifiers to be applied to the trace object. Thus, a trace object is identified by its inherent characteristics, or the identity is calculated using logical models. For example, with some object materials it is possible to identify a trace object by its surface characteristics [7, 8] or to calculate e.g., the localization of a part or object segment based on process parameters via the use of physical-logical relations of the production sequence [9].

# **2 Cell-Specific Identification of Electrode Segments**

Based on the described identification techniques and the requirements for electrode production, all labeling options have restrictions or limitations. Printed identifiers such as 1D barcodes or 2D Data Matrix code transmit printing ink [10], which may influence the battery's performance. Laser-marked identifiers may cause material lesions due to the laser process, and mathematical models have gaps once a part of the electrode is removed. Therefore, a marker-free technique is currently being researched to enable cell-specific traceability.

### **2.1 Marker-Free Identification Using Surface Microstructures**

In the joint project DigiBattPro 4.0, scientists from Fraunhofer IPA and Fraunhofer IPM are working on marker-free identification via existing surface microstructures or direct object features in the area of individual electrode segments.

A camera-based system was designed to uniquely identify individual components without labeling, using only the individual microstructure of the surface. Laboratory tests

**Fig. 2.** Laboratory setup for the evaluation of electrode surface microstructures

showed that aluminum and copper electrodes are suitable for marker-free identification, as shown in Fig. 2.

The marker-free identification system is based on the Track & Trace Fingerprint Technology developed by Fraunhofer IPM. It is a tracking system for mass-produced parts, enabling individual serialization and authentication. The technology is driven by the fact that many semi-finished goods or components have an individually distinct microscopic surface or interwoven color structure. An industrial camera takes highresolution images of selected areas on the component's surface. The specific structural pattern captured by the image and how they are positioned relative to each other is used to generate a numerical identification code, the so-called fingerprint. It is stored in a database and linked with other process and measurement data via a unique part ID. This process can be repeated to identify the component at a later stage in production.

Figure 3 shows an exemplary histogram on the fingerprint recognition of searched components or parts by comparing all fingerprints in a database. The x-axis shows the normalized Hamming distance, a measure of the relative similarity of two compared fingerprints. The smaller the value determined, the more similar the fingerprints are. The y-axis shows the number of fingerprints with the same degree of similarity. A typical successful comparison is shown in Fig. 3, where the sought-after part is sufficiently well separated from all other parts to make a statistically safe and unique identification. The distance between the center of the distribution of all other parts and the searched part in units of the standard deviation of the spread σ is given as a measure of the recognition reliability. When a comparison falls below a certain threshold of these measures, it is regarded as statistically unsafe and labeled as such. Thus, misidentifications are avoided. The value of these thresholds depends on the amount of fingerprints to be compared. A threshold value of 7 σ is sufficient to identify a single fingerprint out of a seven-digit number of fingerprints with statistical certainty.

Up to now, the technology has been implemented in industrial production lines to track single objects [11, 12]. However, the existing identification solution for discrete production objects or pieces does not meet the requirements for continuous electrode

**Fig. 3.** Exemplary histogram with statistical reliability of fingerprint signature recognition. The colors on the x-axis indicate if the sought-after part is identified with statistical certainty (green), identified but with insufficient confidence (yellow) or not identified at all (red).

production. Therefore, further development of the fingerprint system is necessary to close the existing information gaps between the individual processes of electrode production and increase the granularity of serialization (from coil to cell-specific tracing of electrode segments), as well as to prevent material entry into battery cells by marker-free tracing. In continuous material production, a challenge for marker-free identification is the enormous amount of image data that must be dealt with when registering an object for the first time. Fingerprints have to be recorded and stored so that identification is possible, even when taking the image anywhere on the electrode. The developed technology can generate fingerprints within a few hundreds of milliseconds.

To identify cell-specific electrode segments, two cases have to be distinguished. In the first case, identification takes place prior to cutting the electrode. Here, identification can be enhanced by consecutive sequencing of individual cell-specific electrode segments. Furthermore, the system needs to identify if individual segments have been cut out, e.g., due to quality aspects. In the second case, when cutting is already done, it may occur that the order of the single electrodes is lost. As a result, the number of possible candidates is much larger than in the first case. The proposed solution is to compare and process the captured fingerprints on high-performance graphics processing units (GPUs), as this process can be largely parallelized. With recent improvements in GPU performance, processing such data volumes becomes feasible. Thus, the required detection time for identifying fingerprints depends on the hardware system and software configuration. The detection time will be validated and optimized within the planned operation of the fingerprint demonstrator system, enabling the traceability of materials used in continuous processes such as coils or foils.

#### **2.2 Approach for Proving Concept Implementation**

The following chapter describes current developments and the implementation of a traceability system for marker-free identification in continuous electrode production. To develop and evaluate the fingerprint demonstrator system, it is integrated into a rollto-roll coating system operating at conveyor speeds between 0.5 and 20 m/min (see Fig. 4). Substrate foils and electrodes up to 200 mm in width can be handled and treated with infrared radiators and inline calender. The equipment enables the development and testing according to current specifications on lithium-ion production processes defined in advance within the project.

**Fig. 4.** Roll-to-roll coating system with inline calender as basis for the fingerprint demonstrator.

The implementation of the Track & Trace Fingerprint technology consists of at least two reader systems: One reader system scans and serializes the electrode and adds fingerprints to the database (to make them known to the system). The second reader system captures the fingerprints (tracing them in the database) and identifies the electrode segments. Consequently, a fingerprint reader system was developed (see Fig. 5) and two identical systems were set up from industrial computer vision components (see one of them in Fig. 6).

A reader system includes four high-resolution cameras with corresponding lenses, LED bars as illumination devices, and an IT interface to the central fingerprint management software. Stiff mechanical mounts were designed for capturing images without vibrations. Four cameras are needed to track four parallel tracks of electrodes. The sampling of the optical system is limited by the pixel size and amounts to 26,2 μm per pixel. The measuring field of a single image is 70 mm x 21 mm, while operating distance is about 230 mm. The actual area used for fingerprint generation is even smaller at 16 mm x 1 mm, resulting in a memory footprint of less than 1 KB per fingerprint. In the case of adding and tracing discrete parts, a control system such as a programmable logic controller (PLC) sends add or trace commands to the fingerprint system.

**Fig. 5.** Design of the two fingerprint reading systems in CAD, where System A has two possible mounting positions (Pos. 1 and Pos. 2).

When applying in combination with endless material, the reader system itself has to keep track of the coil position and add fingerprints at a fixed distance from each other automatically, independent of the electrode segment length. This is done by image processing of the detected surface microstructure and necessary to ensure the generation of at least one fingerprint in the captured tracing area. The distance between the individual fingerprints varies depending on the camera setup and field of view, fingerprint dimensions, as well as processing times.

A coil with 800 m length requires around 30 million fingerprint comparisons for a single request. As the comparisons can be computed in a highly parallel manner, a GPU acceleration is promising to speed up the process to the required identification rate. Early tests on a low-end mobile GPU already show a doubling in performance compared to the previous system. The objective is to identify cell-specific electrode segments in production cycle time.

A first test setup of the developed system including the lighting system was validated and will be finally implemented on a coating machine. In a first step, the uncoated area of the electrode segment was successfully serialized, identified, and recognized by the developed Track & Trace Fingerprint-based system. In general, all technical surfaces that exhibit an individual microstructure are suitable for the technology. Based on this, further tests will be carried out on different electrode materials together with different coatings (e.g., Cu and Al substrates and graphite coating). In the current state, the system can support up to a 10 m/min feed rate but is projected to reach 20 m/min or more with further optimization. For further use of the fingerprint serialization within subsequent production processes, it is necessary to transfer the fingerprint information from continuous electrode production to discrete cell manufacturing and housing. From the point of cell assembly, this is currently realized by conventional tracking codes such as DMC.

**Fig. 6.** Prototype setup of the demonstrator on a coating machine.

## **3 Conclusion and Outlook**

Traceability of individual lithium-ion battery cells within electrode production is challenging, since there is currently no reliable way of implementing continuous traceability at single-cell level, thus linking electrode production and process data on a cell-specific basis. To meet this challenge and eliminate the existing identification and information gaps between process clusters, this paper established and presented a Track & Trace Fingerprint-based approach for continuous single-cell identification. Based on the newly designed Track & Trace Fingerprint system, a prototype setup was integrated and successfully implemented on a coating machine at the Center for Digitalized Battery Cell Manufacturing (ZDB) of Fraunhofer IPA. The hardware and software system will be further adapted and optimized in the next development steps to optimize the markerfree identification of continuous material. After testing precoated substrates or finished electrode rolls on the current coating and calendaring machine, a further expansion of the Track & Trace Fingerprint system is planned. The integration into additional coating lines allows evaluation of further aspects, such as modified material systems, residual moisture in the coating, and other influences accompanying the wet coating process.

The new marker-free identification approach enables the tracing of electrode segments without material changes and contamination due to the application of printed or lasered identifiers. Even if electrode segments are cut out, it will be possible to allocate electrode process parameters and data obtained during the coating process to the individual battery at cell level. This will provide a more in-depth understanding of the battery production process, a rapid localization of defects in the production chain, and consequently will improve product quality. Quality improvements and determining influential process factors will become possible by a more granular analysis. Besides, new high-quality data sets allow for data-driven production analysis and optimization. With the developed Track & Trace Fingerprint system, cell-specific traceability of lithiumion battery components and process steps to the finished product becomes possible. In the future, the technology can be used for other sectors and continuous manufacturing processes such as the production of continuous materials.

**Acknowledgements.** The research activities presented in this paper have been supported by the German research project DigiBattPro 4.0 - Digitale Batteriezellenproduktion 4.0. The project has received funding from the German Federal Ministry of Education and Research BMBF under grant no 03XP0374C.

# **References**

12. Saum, N., Schmid-Schirling, T.: Rückverfolgung von Bauteilen mit Track & Trace Fingerprint. In: Digital Manufacturing Magazin, pp. 42–43. WIN-Verlag GmbH & Co. KG, Vaterstetten (2021)

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Multi-agent Interaction Structure for Enabling Subsidiary Planning and Control in Modular Production Systems**

Simon Komesker1,2(B) , Jonathan Bartels1,2, Achim Wagner3, and Martin Ruskowski1,4

 TU Kaiserslautern, 67663 Kaiserslautern, Germany<EMAIL_ADDRESS>Volkswagen AG, 30884 Wolfsburg, Germany Deutsches Forschungszentrum Für Künstliche Intelligenz GmbH, 67663 Kaiserslautern,

Germany

4 Technologie-Initiative SmartFactory KL E.V, 67663 Kaiserslautern, Germany

**Abstract.** Modular production systems enable resilient production processes through decoupled production processes. On the way to implementing flexible and adaptable production systems, information support plays a decisive role. Only the use of intelligent and structured information processing across previous system boundaries and areas enables the coordination of requirements and capacities in dynamic production environments. The rigid communication structures in information systems of current production systems therefore need to be replaced by dynamic interaction, both horizontally between entities and vertically between different hierarchical levels. Multi-agent systems (MAS) are one way to meet the requirements for centralized and decentralized decision making in complex (cyber physical production) systems (CPPS). To prepare the instantiation of a MAS, it is necessary to structure and describe the information flows of a production system.

In this paper, the results of a simulation experiment for the implementation of collaborative, subsidiary decision making based on a model-based system structure are presented. Productivity potentials of more than 10% can be shown by using collaborative manufacturing strategies.

**Keywords:** modular production · multi-agent systems · interaction model · systems architecture

# **1 Introduction**

Resilient production systems guarantee robust production processes in the event of unforeseen deviations in the operating sequence. Kern's Modular Production proves to be more resilient to demand and capacity fluctuations compared to linear production systems [1, 2]. With the use of Cyber Physical Systems (CPS), mechanical and mechatronic elements in (socio-technical) production systems acquire a higher decentralized decision-making capability and possess complex interaction [3]. Systems engineering principles enable a better understanding and designing of complex phenomena [4]. Thus, a system structure is needed that enables structured information processing in modular production systems [2].

The requirements for information processing in modular production systems with alternative process design according to Kern have been identified in former research [2, 5]. This paper follows up on these and validates the previous research in the form of a simulation experiment. For this purpose, the related work of preparing structured information processing in multi-agent systems (MAS) for matrix production like Kern's Modular Production is given in Sect. 2, as well as the basics of systems engineering. In Sect. 3, the basic functions for operating a MAS in modular production systems are identified and put together in an interaction model. This model is supported with a system structure in Sect. 4. Section 5 evaluates the implementation before the paper is concluded in Sect. 6.

## **2 State of the Art and Related Work**

In the following section, the related work in the area of systems structures for crosssystem information processing is presented.

#### **2.1 Structured Information Processing in Modular Production**

Modular Production Systems require cross-system information processing [4]. The control paradigm of strictly hierarchical control, e.g. the ISA95 is not suited for dynamic and flexible cross-system information processing [6]. Alternative control paradigms that allow vertical and horizontal communication are hybrid control paradigms [7]. A promising approach to enable hierarchically but flexible information processing is the application of multi-agent systems, that allow for complex communication between encapsulated agents as subsidiary [8, 9]. As previous research stated, a special focus lays upon the systems architecture and structure to support the requirements for information processing between different domains [2, 5]. There are existing architectures that serve as orientation for MAS development [10, 11]. An approach preferably suited for reconfigurable MAS are AOSE methods like ADMARMS which is based on high level design principles and a rather functional oriented development [10].

The coordination of systems is based on interaction of product, processes and resources in CP(P)S [12]. Different possibilities exist to apply coordination by planning and scheduling [13]. These could be used to coordinate systems and its inner systems and elements on different hierarchy levels.

#### **2.2 Structuring Systems for Developing a Systems Design**

A System can be seen as a combination of interacting elements organized to achieve one or more stated purposes [4]. Systems Engineering is focused on the system as a whole, it looks at the system from the outside as well as from the inside using different principles and concepts [14]. The functional concept describes the functions of a system and what a system does. The structural concept describes the interior of the system, as well as the relationships of elements within this system. Elements of a system can also represent systems on a lower level (system of systems) [15]. This nested relation can be described by the hierarchical concept [4, 16]. A subdomain is the systems design, that incorporates the architectural, logical and physical setup. By using a model-based architecting approach a suitable support of informational processes can be created [17, 18].

In addition technical cybernetics analyze the information flow through the system and how it is processed. It considers how this can be used to manage and control itself as a control loop [19].

# **3 Modelling Interaction for Planning and Control in Modular Production Systems**

An Industrie 4.0 compliant systems structure for modular production systems needs to support complex decision making [5].

The procedure for decomposing a system top-down follows a basic principle "From the General to the Detail" [16]. A decomposition is carried out to managerial and operational independence of functions to create a functional architecture [18]. The coordination of functions follows the bottom-up principle and couples different functional blocks to process (steps). As part of the systems analysis, the system's elements are identified as well as the tasks, roles and interdependencies for a system design view [5]. The application of ADMARMS design methodology supports a MAS architecture with strong focus on maintaining the independence of functional requirements [10].

### **3.1 Top-Down Decomposition and Bottom-Up Aggregation of a Modular Production System**

The system is basically designed as a fractal structure with similar subsystems on different hierarchy layers. The subsystems are designed as encapsulated entities, which can be supported by the concept of AAS and ASD [5] (see Fig. 1).

**Fig. 1.** Decomposition of the production system for structured information processing for a holonic architecture

The structuring was done by defining hierarchy layers for the Modular Production by Kern according to the RAMI4.0 levels followed by a functional requirements engineering matching the manufacturing system design. Each system contains similar functional blocks that are encapsulated as holons on different hierarchy levels according to the RAMI4.0 and preparing a subsidiary decision making.

The challenge of modular production systems was identified in the information processing for a resilient production flow in skill-based modular production systems. The functions and basic elements of a system from an information point of view were extracted based on requirements on previous research [2, 5]. They are separated as representation and self-description functions on the one hand and coordination functions on the other:

#### **I Representation and Self-description Functions**

The **Resource Agent (RA)** knows capabilities and availability status of an element or system. It includes skillset, process times and setup matrix. The capabilities or skills are aggregated bottom up from each level to fulfil orders and suborders. The **Product Agent (ProdA)** gives an overview of all manufacturing steps for a specific product in a subsystem. It defines configuration, quality requirements and start and end dates for each item in the production program. It contains information about the order and the manufactured product, with update information from the resources for completed process steps. This interacts with the shell that contains order specific blank options for the product being built. The material is available in sufficient quantity in the supermarket and the transportation is coordinated with the production flow. The production flow manager need the information of material availability for the planned production program. The **Quality Agent (QA)** evaluates quality data from products and parts/material. The **Process Agent (ProcA)** describes skills of the production processes needed to manufacture products with suitable resources.

#### **II Coordination Tasks**

The **Production Flow Manager (PFM)** ensures, that the right processes are performed on the right resources in the right time. Therefore the PFM needs information about the product process steps, the precedence graph and the offers from the resources for processing orders and suborders. The PFM schedules and reschedules the orders on the different resources. The **Production System Manager (PSM)** ensures that the right resources are available to produce the production program. A potential measure could be the reconfiguration of a system e.g. a resource adaption or integration of unplanned orders, because the (sub-)system matches the required skills for that order. KPIs to consider in this context are transportation time, variant flexibility, value add time, setup time, makespan, output. The **Material Agent (MA)** ensures, that the right material is at the right place at the right time and the right volume The **Data Manager (DA)** collects all the actual and requested information in the system and provides a consistent data base. The **Deviation Agent (DevA)** identifies and assesses deviations initiates activities in the system. The mechanism for planning and scheduling is presented in [5] and defines a subsidiary decision making process for multi-level modular production systems. The **Orchestration Agent (OA)** is responsible for the execution of decisions and closing the control loop of an integrated planning and control system.

### **3.2 Interaction Model for Cross-System and Cross-Level Information Processing**

Based on the identified agent functionalities, an interaction model was developed as basis for a functional architecture in the sense of a model-based architecture [15]. The formalized interaction model derived from the functional description in Sect. 3.1 is depicted in Fig. 2. The interaction model is used for every fractal system of the production system to support its subsidiary decision making. A fractal is defined per hierarchy level as a work center, a station or a resource, each represented as a holon (see Fig. 1). The interaction is described on an abstract level as control/command and inform. Control/command interactions need a confirmation of receive, an inform a typical message into an agents inbox. Asynchronous horizontal and vertical communication is realized by the DA that serves as a message broker between agents, structuring data in different topics to which other agents are subscribed and therefore enabling cascaded feedback control loops.

**Fig. 2.** Interaction model for multi-level and cross-system holistic planning and control

# **4 Implementation of a Subsidiary Planning and Control for Enabling a Resilient Production Flow in Modular Production**

In this section a system structure for a use case is implemented based on the interaction model for subsidiary decision making. This is done by using the functional decomposed blocks and aggregate it bottom-up in a multi-level production system.

### **4.1 Introduction of the Use Case**

The production system contains 84 resources to produce 3 different models of a car with different but similar product structure. The system is structured hierarchically in 3 levels according to RAMI 4.0 consisting of fractals designed as holons with the same agent architecture. The **work center level**, that schedules and orchestrates the production of the product. The **station level**, that schedules and orchestrated the production of modules of the product in stations. And **the control device level**, where the processes to manufacture parts for modules are being executed the scheduled sequence. The control device level marks the lowest level of granularity of functions for operational and managerial independence. The production program consists of 300 products, each of which consists of 16 modules and the associated production orders, 3 of which can be manufactured in parallel. The module orders contain 2 to 8 production orders, so that the total number of process steps for a product amounts to up to 90 steps. A data and information model is supporting the function of a skill-based coordination and allocation of the different product needs and resource capabilities with the respective process skills.

### **4.2 Implementation of a Model-Based System Structure**

To create the system structure, the interaction model is prototyped as a holonic MAS. The intelligent medium- and long-term decision-making is realized with the help of agent-based modeling and simulation (ABMS). The coupling with a production system is realized using the industrial-grade Discrete Event Simulation (DES) tool Plant Simulation, which is coupled via a middleware with the ABMS and a scheduling module.

**Fig. 3.** Implemented system architecture from of the interaction model and planning mechanism

The **DES** provides the data basis for the decisions as a simulated production system. Based on the skill-based approach, the resources have the decomposed skills to execute process steps to manufacture a part, module or product. The formerly passive entities of the DES were agentified in order to be able to communicate horizontally and decide decentrally for ad-hoc initiation of transport orders and execution of short-term alternative strategies. The material transport is modeled by freely moving AGV within the station and by lane-bound AGV between the stations.

By using a low-code **Middleware** and TCP/IP based interfaces a synchronous communication between the DES and the MAS can be established. The goal is to connect each passive resource of the DES with an active equivalent in the MAS. The middleware forwards the event-based JSON-formatted updates from the DES to an http-enabled gateway agent of the MAS via the TCP/IP client/server socket interface and returns derived actions, as shown in Fig. 3. Via a web-based user interface, the middleware enables configuration of the MAS, scheduling module and DES.

The **MAS** is the digital representation of the production system and structures the agents within it based on the interaction from Fig. 2. The MAS is a multi-agent system. In implementing the interaction model, the focus was on cross-level processing and functions were aggregated. Based on the FIPA-ACL compliant agent platform JADE, a MAS was developed that implements the sequencing and allocation planning [4]. For this purpose, each control-device level resource was implemented as a holonic CDH in a 1:1 relationship in the MAS. In the prototypical case, each Holon encapsulates several of the agents presented in Sect. 3, within the respective system as a black box. These include the CDH representation function and the DM's function, which interacts with an SQL database. In addition, the function of checking the available skills of resources of the PSM is carried out during initialization and failure events. In the case of levelspecific sequence and allocation scheduling, a scheduling module is triggered in each case, corresponding to the function of the PFM. Through the interaction of individual encapsulated holons as well as the cross-level decomposition of orders, the holons solve the problem of sequence and allocation scheduling subsidiary and forward the resulting plans as executable orders for implementation to the executing control device level of the DES.

The **Scheduling Module** serves as an implementation of the PFM for the subsidiary coordination of resources, processes and products. The scheduling problem of the use case describes an extended flexible job shop problem. The scheduling module for solving the problem was implemented in Python and solved using a heuristic based on the Tabu-Search method. The holons use the module to perform an allocation of the hierarchically distributed operations of a task with the resources available in the respective holon. The agents have the possibility to use the heuristics of the module when requesting an offer as well as to optimize the final schedules by the heuristics and to forward concrete orders based on them. The scheduling module optimizes the schedule according to the lead time.

The developed system structure represents a production system as **a system of systems,** which is able to solve subsidiary decisions on a short-term decentralized shop-floor level as well as medium- and long-term planning problems through the coupled MAS. After an initial decomposition of the planning problem in the MAS, a sequence plan is generated by the holons communicating with each other. The final production orders are derived and forwarded to the DES resources using the middleware. The ad-hoc transport planning as well as the short-term decentralized reaction of the resources to disruptions is realized by the horizontal communication in the DES.

### **5 Simulation Setup and Results**

In this section, the configuration and the corresponding execution of the simulation runs are explained in more detail. In a first step, the different variables are presented as well as the evasion strategies of the resources on control-level-device and station-level within the simulation runs.

The overall layout of the DES, consisting of 84 control-devices containing skills grouped into 16 stations in 1 work-center is designed as a matrix. The production program of 300 products and three types is initially planned for 110 products and then released in groups of 30 products when the number of products in the system is lower than 80 products. The buffer capacity of resources on control-device-level is limited to 2 with an availability of 95% with an MTTR of 2:30. The transport within each station is realized by 5 AGV, for inter-station transport with 40 AGV. The station buffer capacity is set to 20.

**Fig. 4.** Exemplary graph of three different simulation runs following different manufacturing strategies

The simulation runs follow different manufacturing strategies to show the effects of different collaborative decentral and central control mechanisms. Three strategies *strict (strict plan execution), local (local negotiation for alternatives) and global (global negotiation for alternatives)* were followed to simulate different situations of deviation events at control device or station level. The *strict* strategy follows the initial plan from the scheduling tool and does not allow alternative process sequences. The *local* strategy allows to reassign a product in case a resource failure occurs. In this case, the alternatives that the technical precedence graph allows are evaluated. Either the process step can be completed in the same station at a different resource with the required skill or a different order with a different skill requirement can be assigned. Compared to the *local* strategy, the *global* strategy additionally allows the selection of another station within the overall system to process the open order by using the agent interaction for negotiation of alternative resource allocation and product sequences.

While the lead time (LT) for 300 products of the *strict* simulation runs is 7670 s on average, the LT decreases to 7313 s in case of the *local* strategy. The *global* strategy reduces to an averaged 6891 s. The transportation time percentage (AVG\_TP) with the *global* strategy of all products are on average 2.8% higher than with the *strict* strategy. At the same time, the storage time percentage (AVG\_SP) of the products are reduced by 5.8%, which results in an overall increase of the percentage of value-added production time (PP) by 3%. Comparing the lead times for the mentioned production program, this results in an average reduction of the LT of 11.2% for the same product mix and the same incident volume. Figure 4 shows an exemplary graph of lead time of products LT leaving the system in an exemplary graph. In the start-up phase of the system, the strategies do not differ due to low competition for resources. After the ramp up phase, the saving corresponds to about 11.2% (Table 1).

**Table 1.** Results of average makespan [overall\_time], transportation time percentage [AVG\_TP], average storage time percentage [AVG\_SP], average value-added production time percentage [AVG\_PP] and average leadtime [AVG\_LT] of the different manufacturing strategies in 27 simulation runs

# **6 Conclusion**

This paper shows a system structure for holistic information processing with centralized and decentralized intelligence. This is based on an interaction structure for cross-system (horizontal) and cross-level (hierarchically) interaction for an autonomously organized production flow. A hybrid control paradigm was implemented using MAS and shows its potential for the use of centralized and decentralized planning and control in a use case of a modular production systems. This allowed the implementation of an agentbased planning mechanism that realizes sequence planning over multiple levels. The planning and control mechanism was designed using an interaction model for a holistic support. For the execution of the planning and control in dynamic environments a hybrid decision support is needed, which was prototypically implemented in a simulation experiment. The interaction model was implemented focusing on cross-level interaction and collaborative decision making between holons of different systems in a MAS. The collaborative decision making based on cross-level and cross-system interaction in a holonic MAS enables an optimization of the lead time by more than 10% for dynamic production situations. Agent based scheduling and control is used in different scenarios in the experiment. Firstly, a global optimal schedule for a multi-level production flow was created by negotiating between different local optima in the manufacturing system. Secondly, during manufacturing execution the agents optimize the global production flow autonomously by cross-system and cross-level interaction in system. By using the interaction structure, an alternative production flow is created and orchestrated, which solves unforeseen events e.g. deviations caused by machine failures. In future experiments, the interaction structure will be thoroughly validated by adding additional agents to the holonic architecture. For further research, additional scheduling methods can be applied as well as further enhancements of the cascaded control loops to allow a more dynamic and predictive assessment of deviations and possible risks.

**Acknowledgements.** This work was supported by the European Union under the "Horizon 2020" research program (Grant no. 957204) within the project "Multi Agent Systems for Artificial Intelligence" (MAS4AI).

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# **Resilience to Pandemics through Flexibility in Sourcing, in Order Fulfillment, and Production Capacity of the Automotive Supply Chain**

Marc Gebauer1(B) and Cyrine Tangour2

1 Brandenburg University of Technology, Platz der Deutschen Einheit 1, 03046 Cottbus, Germany

<EMAIL_ADDRESS>
2 Fraunhofer-Zentrum für Internationales Management und Wissensökonomie, Neumarkt 9, 04109 Leipzig, Germany

**Abstract.** Since the COVID-19 pandemic, the automotive industry which is regarded as a best practice considering its supply chain has experienced new threats which render its supply chain vulnerable. For instance, the many lockdowns, associated with collapsing of global distribution channels sunk vehicle sales dramatically. The purpose of this study is to identify capabilities to strengthen the resilience of automotive supply chains to pandemics. By using the supply chain resilience framework developed by Sytch et al. [4] we analyze the resilience of the automotive supply chain with evidence from the literature to a pandemic crisis with vulnerability factors similar to those of COVID-19.

We do find evidence that seven out of ten capabilities we looked for are present in the automotive supply chain. Capabilities to improve are i.e., multiple sources for tier 1 suppliers, improving risk pooling/sharing, and defining means of production postponement.

With the evidence on resilience factors for pandemics, we provide managers with a set of factors to focus on in pandemics. Thus, our study helps managers to better prepare their supply chain to resist global crises such as the COVID-19 pandemic. We used a methodology that can be applied with more secondary and also primary sources and therefore is interesting for researchers.

**Keywords:** resilience factors · vulnerability factors · supply chain · automotive industry

# **1 Introduction**

The COVID-19 pandemic, which started in China in December 2019 uncovered many vulnerabilities of global supply chains [1, 2]. Many countries closed their borders to travelers and goods transport – by air, land, or sea- to limit the spread of the virus on their territories [2]. Direct consequences are the collapse of global distribution routes, economic self-isolation, "consumers reduced income" and "production shutdowns" [3], which impacted both the demand and supply sides of supply chains worldwide [2].

Because of its heavy reliance on a fragmented and global supply chain, the automotive industry is a typical sector that got hit heavily by the COVID-19 outbreak [2–4]. Major disruptions, such as the COVID-19 pandemic, result in deep cuts in sales for production companies like the automotive industry (e.g. [5, 6]). For instance, vehicle sales sunk by almost 20% in February 2020 compared to the previous year Free and Hecimovic [2] relate that in the USA, 93% of all automotive production was stopped by late March 2020. In Europe, it is estimated that 1.1 million workers in the automotive industry lost their jobs because of the pandemic [7].

The automotive supply chain is highly vulnerable to a turbulent external environment, consequently to its complex and globalized supply chain which is associated with higher uncertainty than other sectors' supply chains [8, 9]. Managers estimate that supply chain risks constitute the most severe threat to their company [10]. Consequently, managing risks and uncertainty of supply chains is an essential ability in the automotive industry [9], which can be understood as the resilience of supply chains.

Starting in the middle of the 1990s, researchers began to develop approaches to increase the resilience of supply chains to disruptions [11]. First, they identified resilience approaches to improve supply chain efficiency, such as just-in-time production [12]. Since the COVID outbreak, the focus of research on the resilience of the supply chain to various vulnerability factors grows strongly. A search conducted on 27th May 2022 on the Web of Science database with the terms "resilience" and "supply chain" results in eight publications for 2010 and 573 publications for 2021. Resilience in a general meaning is the ability to resist disruption. The resilience of supply chain research is segmented into at least five fields, namely: (1) types of disruptions, (2) resilience phases, (3) resilience strategies, (4) resilience methods and tools, and (5) capabilities for resilience. While research in each distinct field is abundant, research that crosses fields is less frequent. In the study of Sytch et al. [4], a framework is conceptually developed that bridges the research fields of type of disruption or vulnerabilities and the capabilities for supply chain resilience. We use a qualitative content analysis approach in combination with the framework of Sytch et al. focusing on capabilities for resilience to future pandemics based on information from scientific literature. To what extent are the capabilities of the automotive industry resilient to vulnerability factors related to a pandemic situation similar to COVID-19? The focus is on flexibility in sourcing, in order fulfillment, and production capacity of the automotive supply chain as main capabilities to be resilient to a pandemic.

The paper is structured as follows: After the introduction, we provide an explanation of the resilience of supply chains followed by remarks about the resilience of the automotive supply chain, recommendations for the automotive supply chain based on our analysis, and the conclusion.

# **2 Resilience of Supply Chains**

A generally agreed-on definition of supply chain resilience has not yet emerged in the literature [11]. It can be constructed from its elements "resilience" and "supply chain". Resiliency in general is "…the capacity for an enterprise to survive, adapt, and grow in the face of turbulent change…" [13] and "…the ability of a system to return to its original (or desired) state after being disturbed" [14]. This system can be a company's supply chain. It is defined as "…the network of companies involved in the upstream and downstream flows of products, services, finances, and information from the initial supplier to the ultimate customer" [15]. Thus, we do define supply chain resilience as the ability of the supply chain to return to its original state (measured in dimensions of performance) or a higher one after being disturbed. The literature on supply chain resilience is divided by a) phases, i.e., pre disruption, during disruption, and post disruption, b) strategies, which can be proactive, concurrent, or reactive, and c) the capabilities to anticipate, adapt, respond, recover, and learn. [16] This study focuses on capabilities. Companies can be resilient by developing capabilities (**Ci**) to overcome the vulnerability factors (**V**j). In this case, resilience factors (**Vj, Ci**) are present as shown in Table 1 [4]. Vulnerability factors are turbulences, deliberate threats, external pressures, resource limits, sensitivity, connectivity, and supplier/customer disruptions. And capabilities factors can be flexibility in sourcing, flexibility in order fulfillment, capacity, efficiency, visibility, adaptability, anticipation, recovery, dispersion, collaboration, organization, market position, security, and financial strength [15]. This study's focus lies on flexibility in sourcing which is "the ability to quickly change inputs or the mode of receiving inputs", flexibility in order fulfillment meaning the "ability to quickly change outputs or the mode of delivering outputs" and capacity which is the "availability of assets to enable sustained production levels" [15] since flexibility and capacities have been found to be effective strategies for reducing the effects of disturbances [17].

**Table 1.** Framework for resilience factors (Source: own table based on [6])

### **3 Supply Chain Resilience of the Automotive Industry**

#### **3.1 Procedure**

For the collection of data, the method of a systematic literature review is used for which the Search-Appraisal-Synthesis-Analysis (SALSA) framework of Booth et al. [18] is followed. To capture more relevant studies, two search levels are defined. First, one of the largest scientific databases, Web of Science, is systematically searched by using the following predefined keywords: [TS = Resilience AND automotive AND "supply chain"]. The search scope is limited to only peer-reviewed academic publications in English or German with no timeframe limitations. 38 papers are first found. Their appraisal is conducted through a streamlined deductive qualitative content analysis of the full-text versions. Thus, the main selection criteria is the availability of information to be extracted to fill the theoretical framework table. Since not all information is found in the selected papers, thus, a second search was conducted on the more inclusive scientific database Google Scholar. The aim is to find other secondary sources, such as industry reports or conference proceedings, to complete missing information of the theoretical framework. More papers are selected, which rises up the number of secondary sources selected for this study. The data from the secondary sources are summarized in a table. This table is the base for the analysis in this study. It shows the specific capabilities that are present or absent in the current automotive industry, with respect to flexibility in sourcing, order fulfillment, and production capacity.

Data analysis and recommendation generation are conducted by two researchers independently, based on the filled theoretical framework table. Both outcomes of the analysis and recommendations are then compared and discussed. Differences are argued and the consensus is reached. The results are presented in Sects. 3.3 and 4.

#### **3.2 State of Art of the Automotive Supply Chain**

The automotive supply chain produced 80 million cars including commercial vehicles in 2021 and results in a yearly turnover of 800 million e for only the top 100 suppliers [19]. It spreads over 67 countries and is therefore a global supply chain [4]. A global supply chain is to be understood as

"[r]aw materials and intermediate goods are now frequently shipped across the globe several times before final products are exported to final consumers around the world, coordinated by accounting technologies predicated on global labor arbitrage, cost minimization, lean inventory management, and tax avoidance" ([2, S. 2] based on a literature review).

On the one hand, the automotive industry is seen as "the leader in supply chain management" [20, S. 704]. This results in automotive supply chains being well researched (for instance [6]). We find research about the automotive supply chain and its resilience in Japan, Thailand [21], China [22], Iran [23], Brazil [24], Turkey [25], Portugal [17], and Germany [26]. On the other hand, the automotive supply chain has been hit heavily by the Covid-19 pandemic. "…automakers' supply networks are not nearly as robust […] to global disruptions [like the] pandemic" [4, S. 129]. Reasons for the vulnerability of the automotive supply chain lay in the focus on cost reduction and efficiency [17] as a goal reached by lean management, just-in-time delivery, and outsourcing [27, 28]. The analysis of automotive supply chains generally results in the structure of an OEM, 1st-tier suppliers, 2nd-tier suppliers, and more entities. General Motors counts 193 unique tier-1 suppliers, 899 unique tier-2 suppliers, and 2,875 unique tier-3 suppliers. For Volkswagen, the numbers are 213, 1,026, and 2,901 ([4] with data from Bloomberg). More specifically companies in the automotive supply chain act as supplier of parts and materials, are assemblers, logistic service providers or specialize in research or testing. Additionally, regulatory bodies are included [26]. Thus, it can be considered complex [17] providing also complex products compared to other global supply chains [29] (see Fig. 1).

**Fig. 1.** Supply chain mapping for the global automotive industry [29]

### **3.3 Sourcing and Fulfillment Capabilities of the Automotive Supply Chain**

In summary, only seven of the ten capabilities we focus on in this study are found in the current automotive supply chain (see Table 2).

**Table 2.** Capabilities of the automotive supply chain from the literature (Source: own table)

We did find evidence for most of the resilience capabilities belonging to flexibility in sourcing, flexibility in fulfillment, and capacity in the literature on automotive supply chains. Starting from the beginning of automotive production until now parts, e.g. axles or wheels are used partly as standards for different products to cut costs [6, 30]. Thus, we do find evidence for the capability part and input commonality. Considering the modular product design which is meant to keep control of the growing variety of products [31]. The supplier contract flexibility can be found in the automotive supply chains for the quantity of supply, e.g. of engines [32, 33]. The use of multiple sources is not really found for the tier-1 suppliers. Toyota, as an example, "interacts directly and closely with the first-layer suppliers […] [s]ince such key components are customized, only a few pre-selected vendors are in its first layer…" [6, S. 219]. The other layers with less specialized components tell a different story. Here we do find cases in which "[i]f one supplier fails […] still at least one [will be available] ensuring the delivery of parts" [26, S. 245]. Going further with the attributes for flexibility in fulfillment cases of alternative distribution channels can be found. One exemplary company "…has a nationwide network to facilitate the customers. This network includes 349 dealers and their workshops, 1541 authorized service centers covering 897 cities and towns" [34, Abs. 3.2]. Additionally, the means of transportation have been changed in one case after the attacks on the twin towers [35]. For risk pooling or sharing as another resilience capability and delayed commitment and production postponement, we did not find evidence in the literature. Lastly, the elements of capacity, reserve capacity, and redundancy are generally found present according to examples from literature, such as safety stocks [24, 26] and "…manufacturing presence in multiple countries…" [4, S. 125].

### **4 Status Quo and Recommendations**

Based on the COVID pandemic, four subfactors are defined to describe the vulnerability that can result from a pandemic crisis, namely: barriers to import and export, lockdowns, the unpredictability of demand, and fluctuations in currencies and prices. The effect of the current capabilities of the automotive supply chain (see Table 2) on the mitigation of vulnerabilities subsequent to a pandemic is evaluated. The results are summarized in Fig. 2 and detailed in the text hereafter. Based on these findings, a recommendation for a higher resilience of the automotive supply chain is drawn.

Parts/components commonality (C1) and modular product design (C2) have similar effects on the resilience of the automotive supply chain. The capability of the automotive industry of using parts and components from more than one product or product families decreases the resilience of its supply chain to a barrier of import and export in which the delivery of parts by global suppliers is interrupted. Furthermore, lockdowns, in which workers are not allowed to go to factories, lead to the interruption of the production of parts. When parts are common to many products, such is the case with the automotive industry, by ripple effect the supply chain is disrupted. Similarly, Modular product designs decrease automotive supply chain resilience to barrier of import and export and lockdowns. Especially in European and USA, the automotive industry tends toward a modular product design, in which they outsource car modules from 1st-tier suppliers [6]. The resilience of their supply chain is highly reliant on suppliers of modules (i.e., 1st-tier suppliers). Modular product design and parts commonality increase the resilience of the automotive supply chain to the unpredictability of demand, as they increase their flexibility in adapting their production lines to fluctuating demand. No discernable effects on the resilience of the automotive supply chain to fluctuations in currencies and prices could be identified.

Supplier contract flexibility (C3) and reserve capacity (C6) have similar effects on the resilience of the automotive supply chain. In a flexible supplier contract, it is agreed

**Fig. 2.** Diagnostic of the resilience of the current automotive supply chain to COVID-19-like pandemic crisis (Source: own figure)

that a predefined amount of an order can be revised up or down. Currently, flexible supplier contracts are used by defining a corridor in which the volume to be supplied can vary [32]. Such flexibility increases undeniably the automotive supply chain's resistance to all vulnerability sub-factors associated with a pandemic situation. According to the literature, automotive firms use different tools to define safety stock levels to mitigate crises [24, 36]. This capability increases the automotive supply chain's resilience to a pandemic situation in all four aspects.

Multiple sources for tiers 2 and 3 suppliers (C4), alternate distribution channels (C5) and redundancy (C7) have similar effects on the resilience of the automotive supply chain. In general, the capabilities of flexible sourcing through alternative suppliers of parts mitigates disruption from the barrier of import and export, lockdowns, and fluctuations in currencies and prices. Similarly, the capability to use flexible ways of transportation and to switch transportation means, when necessary, allows the automotive supply chain to mitigate barrier of import and export, lockdowns, and fluctuations in currencies and prices. Finally, redundancy, such as by establishing manufacturing facilities in many countries [4], increased the resilience of the automotive supply chain to the barrier of import and export, lockdown, and fluctuations in currencies and prices. The effect of multiple sources, alternate distribution channels, and redundancy is not discernable on the unpredictability of demand.

Based on the results of this paper, we recommend the following. First, to increase the resilience of the automotive supply chain, the three capabilities that do not exist in the current automotive supply chain should be developed, namely: (1) multiple sources for tier 1 suppliers, (2) risk pooling/sharing, and (3) production postponement. As tier 1 suppliers produce and supply more critical parts of a car than tier 2 and 3 suppliers, the effect of relying on multiple sources would be more pronounced on supply chain resilience. It is recommended that automotive assemblers should have more than 1 tier supplier to increase supply chain resilience to the barrier of import and export, lockdowns, and fluctuations in currencies and prices. Furthermore, risk pooling and sharing is a key capability to mitigate vulnerability due to fluctuating demand and prices, supply and demand uncertainty [37], such as the barrier to import, export, and lockdowns. Moreover, the capability of production postponement allows to shift manufacturing activities across different products [37], thus allowing companies to be resilient to all four vulnerability aspects associated with a pandemic situation.

When taken apart, two capabilities render the automotive supply chain more vulnerable to pandemics which are parts commonality and their modular product design. At the same time, these capabilities are a cornerstone of the high efficiency of the automotive supply chain. The recommendation is to find a balance between high efficiency and high resilience of the automotive supply chain, by considering key capabilities in connection with each other. For instance, parts that should be common to many product lines should have multiple suppliers and redundancy in their manufacturing and storage. In that way, the capability of parts commonality (C1) is moderated by multiple sources (C4), reserve capacity (C6), and redundancy (C7).

# **5 Conclusion**

The pandemic shows vulnerabilities in the automotive supply chain which can be regarded as one of the best. Parallelly, the complementary interest in research in supply chain resilience has been growing in the last years. The literature presents us with concepts, simulations, and with some empirical studies about supply chains being disturbed by vulnerabilities. Our study provides evidence from the literature that the automotive supply chain could improve its resilience by finding multiple sources for tier 1 suppliers, improving risk pooling/sharing, and finding possibilities for production postponement. These recommendations are based on a single literature study. Thus, they offer orientation but need to be weighed carefully by managers. Further research can be done with different secondary or new primary data.

# **References**

37. Dehdar, E., Azizi, A., Aghabeigi, S.: Supply chain risk mitigation strategies in automotive industry: a review. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, pp. 84–88, December 2018. https://doi.org/10. 1109/IEEM.2018.8607626

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Requirements for a Process to Remanufacture EV Battery Packs Down to Cell Level and Necessary Design Modifications**

Melina Graner1(B) , Frieder Heieck1, Alexander Fill2, Peter Birke2, Woidy Hammami3, and Katharina Litty3

1 Hochschule Für Angewandte Wissenschaften Kempten, Bahnhofstraße 61, 87435 Kempten,

Germany

3 Daimler Truck AG, Mercedesstraße. 128, 70327 Stuttgart, Germany

**Abstract.** In case of electric vehicles (EV) powered by lithium ion traction batteries (LIB), remanufacturing processes nbecome increasingly important due to their rising market share and valuable raw materials. LIB can account for up to 40% of the total EV cost. Often, only a small portion of the cells are significantly degraded when the usable battery capacity falls below 80%, which is currently considered the standard end-of-life criterion. However, in order to enable efficient remanufacturing, novel battery design principles are required. This paper discusses the requirements, opportunities and challenges of future remanufacturing processes of LIBs down to the cell level using a battery system of a commercial vehicle as an example. It gives an overview of the current state-of-the-art manufacturing processes of battery systems and shows the developed overall remanufacturing process including condition assessment, disassembly and reassembly. Subsequently, requirements on future designs are discussed. The state-of-the-art of EV batteries is evaluated based on these requirements to determine where incompatible connections such as welded contacts or adhesive joints conflict with remanufacturing design principles.

**Keywords:** Remanufacturing · EV battery · Condition assessment · Modular design · Circular economy

# **1 Introduction**

Battery technology enables the transformation of the mobility sector towards sustainable drive systems. Electrical driving technology appears to be the future of transportation and offers climate and eco-friendly transportation of passengers and goods. EVs are mostly equipped with LIBs. With increasing market share of EVs, the question about how to process a battery after its usage time arises and processes for the recovery of materials and components such as recycling and remanufacturing gain importance. The industrial research and development (R&D) efforts in battery design mainly focus on the increase of energy and power density, cost reduction, fast charging and improved safety [1]. Tremendous progress has been made in the optimization of battery design on the material level (material for cathode, anode etc.), electrode level (e.g. electrode thickness), cell level (e.g. shape) and system level (mechanical design, battery management system (BMS) etc.) [2]. Concepts for a recycling-oriented design and manufacturing received less attention. In current LIB-systems, single battery cells are connected to form a battery module. Several modules together with additional electrical periphery (e-parts like battery management etc.) form a complete traction battery.

The research gap addressed is the concept of a remanufacturing process for LIBs down to cell level and the associated changes regarding design and assembly of the components. This paper first provides a state-of-the-art review on LIB systems with respect to their life cycle and upcoming recycling directives. Second, an exemplary LIB system with prismatic cells is analysed with respect to the manufacturing process and applied joining techniques, which are decisive for an efficient remanufacturing process. Third, the challenges of the current system regarding an efficient and potentially automated remanufacturing process and necessary design modifications are discussed.

# **2 State-of-the-Art/Literature Review**

This chapter provides a review of the relevant contributions from the existing body of scientific publications, legal regulations and articles in the fields of LIB design, aging behaviour of components and their treatments after end of life back to the year 2006. The European Directive 2006/66/EC takes producers of batteries or producers of components incorporating a battery into responsibility for the waste and recycling management of batteries that they place on the market [3]. A recent update of the directive requires recycling efficiencies and recovery of materials for batteries of 65% by 2025 [4]. Even though there are legal regulations for the material efficiency, there is currently no standardised procedure for the processing of returned batteries. When an EV battery reaches its end of first life, manufacturers have three options: disposal, recycling, or reuse. In most regions, regulation prevents mass disposal. Established recycling processes currently only focus on the highly valued materials such as cobalt and nickel [5].

**Fig. 1.** The life cycle of a LIB includes the battery design, manufacturing, usage life and either a second life or direct recycling of material after the end of first life.

Pyrometallurgy (smelting) offers only a low recycling rate with high-energy input, as cheap base metals such as lithium, graphite or carbon get lost in the process. Hydrometallurgy (leaching, precipitation and resynthesis of elements) uses huge amounts of chemicals, which require professional disposal. New processes that recover more material are not yet fully mature. The third option is the reuse of batteries in stationary energy-storage applications with lower current and energy density demands. However, second-life applications can only extend the lifespans of LIBs. Critical materials still need to be recycled after the end of second life, as it is shown in Fig. 1 [6, 7].

A LIB drops out of first life when it no longer meets EV performance standards, which typically means maintaining 80% overall capacity [5]. It is possible, that the total capacity is determined by a few degraded cells. Kampker et al. carried out an experimental confirmation of cells reliability in 2021. A LIB was disassembled after roughly 288 deep cycles and all 196 cells were tested, resulting that 89% of the cells were still reusable whereas 9% had an insufficient capacity for reuse and 2% showed other types of failures. Of the reusable cells, 68% were probably still useable for automotive applications, 16% only for stationary applications, and 5% were probably not worth being reused, even if they did not fail [8]. These results confirm a simulation by Mathew et al. [9], which estimates that most cells of a used battery pack are worth recovering. The virtual replacement of 5%–30% of the worst aged cells resulted in a restored state of health (abbreviated SOH) of almost 100%. These results, viewed in the context of increasing scarcity of resources and the legal regulations aiming for circular economy for LIBs raise the question how single cells can be restored and reused.

Remanufacturing transforms a degraded product into a quasi-new or an improved functional state by reprocessing and exchanging single components, thus increasing its resource and economic efficiency and enabling circular economy principles. Remanufacturing on LIBs means the exchange or preparation of degraded components or cells. According to the case study scenarios of Alfaro-Algaba et al. [10] and the results of Kampker et al. [11], remanufacturing of LIBs offers great potential in economical and ecological savings. However, there is currently no industrial application of reprocessing LIBs down to cell level. This is due to designs unsuitable for an automated disassembly and varying designs from different manufacturers. Upcoming industrial trends like the cell-to-pack design (with cells directly integrated in the housing without stacking to modules) intensify these problems or simply do not allow remanufacturing [12].

### **3 Analysis of a State-of-the-Art Battery System**

This paper focuses exemplarily on the LIB system of a commercial vehicle. In the e-axle of a commercial vehicle multiple battery packs are combined to reach higher battery capacity for higher power and range. Nevertheless, the composition and components of the LIB system itself are comparable to LIB systems in passenger cars.

#### **Methodology**

Based on an overview of the current manufacturing process of LIBs derived from stateof-the-art commercial vehicle LIB systems as well as from a literature review, this paper shows a process step sequence for a remanufacturing process. Various requirements for an effective implementation of this process model are defined. Subsequently this publication discusses requirements on future designs, which enable the processes derived earlier. Furthermore, it evaluates the exemplary battery design based on these requirements. Figure 2 represents the procedure graphically.

**Fig. 2.** Methodology of the research.

#### **The Manufacturing Process of a State-of-the-Art Battery System**

Figure 3 describes the manufacturing process of the exemplary LIB system (Fig. 4), both derived from a commercial vehicle's LIB system as well as a literature review.

**Fig. 3.** The manufacturing of a state-of-the-art LIB transferred from [13] to the system in Fig. 4.

An initial test sorts out defective cells using impedance spectroscopy, voltage measurement or capacity analysis. The single prismatic cells are enclosed with adhesive foils for isolation and are combined to cellblocks and clamped in a module frame. A welded ladder rail contacting system interconnects the cells and connects all cells of a module with the cell monitoring board (CMB for voltage and temperature, mounted to the module block). The modules are interconnected with busbars. The most common electric contacting technologies are laser welding, laser bonding or ultrasonic welding [15]. The following is a test of conductivity at the joints. For later connection to the master system, the modules are equipped with cable harnesses. Before positioning the modules in the housing, a cooling plate is placed on the bottom of the pack. Thermal glue or foam between the modules and the cooling plate and additional screw connections to the cross bars of the housing tub secure the module blocks [16].

In the exemplary LIB system in Fig. 4 two layers of modules are arranged in separate housings to increase the battery capacity. They are placed on top of each other, separated by a solid sealant. The topcover closes the housing with screws and a solid sealing. A leak test guarantees its tightness. On top of the two module layers there is the EE-Box (containing electric/ electronic peripheral devices such as contactors, fuse, pyrofuse and busbars to which the wiring harness is attached). It is mounted to the housing and also connects the high-voltage (HV) connector, low-voltage (LV) connector and master BMS. A liquid cooling system for all packs is mounted on one of the packs (not displayed in

**Fig. 4.** Schematic design of an exemplary LIB and its components (left) and a module (right).

Fig. 4). The mounting of those components is detachable as usually screws, clips or plugs are used [16].

### **Remanufacturing Scenarios for LIB Packs**

The remanufacturing process is not simply the reversal of the manufacturing process, due to various inextricably designed connections as well as degraded components. Based on the generic design presented in Fig. 4, it can be described by the following steps: analysis and condition assessment, removal process, check and preparation of single components and cells and reassembly/ replacement of components.

**Fig. 5.** The developed remanufacturing process for LIBs with different strategies adapted and supplemented from [16].

The first step is to discharge the packs. After that, a Begin-Of-Line Test (BOL) estimates the overall condition of the pack and identifies defect components or cells. The SOH and aging condition of single cells decide about their further usage. The same applies to the functionality of the e-parts, some of which could use an overhaul.

The process sequence depends on the chosen business model or remanufacturing strategy. Figure 5 indicates the strategies (a) with an exchange and replacement of aged modules or (b) aged cells. Strategy (c) means disassembly of the whole system, where components are used as spare parts or in newly reassembled packs. Also a combination of these strategies is possible: if a module contains a single or few degraded cells they are replaced, otherwise the whole module is exchanged. Some of the remaining cells from the removed module then might serve as spare parts. Similar to strategy (c), Kampker et al. [13] introduce a remanufacturing architecture to build new LIB systems from disassembled components. Strategy (a) is similar to the business model of the Nissan Leaf, where aged modules of LIBs can be replaced- enabled by a special design with very small modules and bolted connections [14].

## **4 Remanufacturing Challenges and Design Modifications**

#### **Challenges and Requirements of Remanufacturing**

Due to the wide variety of cell-, module- and pack-types, today the dismantling of LIB systems is time-consuming manual work. Considering the rising amount of returning LIBs (one million are expected to return by 2028), the remanufacturing process needs to be partially or fully automated to implement it industrially and economically [15]. Figure 6 indicates that the expected high number of returning LIBs will require automated processes. It must be further investigated whether a flexible line in terms of variants and quantities is better than an automated sub system separated by cell type (cylindrical, pouch or prismatic) or process steps (e.g. glued or screwed connections).

**Fig. 6.** Different levels of automation in production systems based on the illustration in [15].

To generate the quantities, a well-targeted and systematic collection has to feed the returned LIBs to the remanufacturing process. This also means that batteries from damaged vehicles are handled in different processes due to potential dangers.

A standardised labelling of LIBs supports the idea of automation, so the used materials and joining technologies can be identified ahead for the flexible systems to react or set up accordingly. This is similar to the idea of a battery passport that carries helpful information about the manufacturer, design, data sheets and disassembly manual [17].

A reliable BOL-test needs to identify damaged packs to avoid safety issues or problems in disassembly and to access the quality. LIBs not worth for remanufacturing will be treated differently. In addition, the BOL should evaluate the state of charge.

A safety concept should define the treatment of packs in case of a thermal runaway. Michaelis et al. suggest a standardised BMS-interface to extract life cycle data and information about the usage profile, possible failures in the pack and the SOH [16]. The condition assessment needs reliable, standardised, affordable and non-destructive analytical methods [17]. In addition, reliable inline measurement principles for cell checks and their classification as well as for periphery e-parts need to be developed. Lately a cooperation between Audi and Volkswagen released a diagnosis tool, which assesses the overall condition of the LIB pack and single cells within a few minutes [18].

For operational safety regarding high voltage and states of stress in the components, specially trained staff uses insulated and securing tools. A crane helps handling heavy components, as a pack can weigh over 700 kg in case of heavy-duty vehicles.

Short-circuit hazards also have to be avoided and the process forces and positioning of grippers and tools need to be very precise. The highly sensitive cells and sensors need to be separated but not damaged by mechanical stress or thermal load. Tolerance chains have to be considered in the resulting stress conditions and expansion behaviour.

Looking closer at the LIB design in Fig. 4, the previously described manufacturing process is not reversible due to the applied production methods and materials. The irreversible steps of the manufacturing process require either a non-destructive dismantling technology or a change in the manufacturing process going hand in hand with the redesign of the battery system itself. As the results of the disassembly experiments of LIBs with prismatic cells of Kampker et al. [8] and Schäfer et al. [19] show, there are no such non-destructive dismantling technologies down to cell level with potential for automation. Obviously, adaptions in the design of LIB systems are necessary.

### **The Main Difficulties of Disassembly of State-of-the-Art LIBs to Cell Level Are:**

#### **Requirements on Future Designs for Remanufacturing:**

LIBs with new designs must be comparable to the state-of-the-art LIBs regarding performance, weight and dimensions. Alternative electrical contacting methods should not have higher electrical resistance than on state-of-the-art batteries. Based on the literature review and analysis of the exemplary LIB system, Table 1 suggests R&D activities regarding innovative materials or integration of functions to improve the potential of future LIB systems for economical remanufacturing scenarios. The overall design should be modular with interfaces for automated disassembly. As few components as possible should be destructed during the disassembly. Inseparable bonds such as welding or gluing should be avoided or at least made in such way that they can be separated.

**Table 1.** Design modifications for a remanufacturing friendly LIB design

Based on the above-mentioned requirements, the exemplary design of a state-of-theart LIB system in Fig. 4 is evaluated regarding suitability for remanufacturing based on the used materials and joining technologies.

The EE-Box including HV-Connector and LV-Connector can be easily removed from the housing even though corrosion over the usage time can complicate the loosening of screws, plugs and clips. Wiring and cable harnesses of the BMS Master and the HV- and LV-module can be disconnected. The design of the topcover and the housing enables an opening of the battery pack.

The busbars for connecting the modules are permanently welded. A separation is only possible with the removal of material (such as drilling or milling of the contacts). To lift out the module (frames) first the screwed connections to the pack housing have to be loosened. The thermal glue or foam between the cell blocks and the cooling plate complicates the removing of the modules and is the cause of possible damages.

Similar to the module level, the welded connections for contacting the cells and CMB cannot be separated non-destructively. Laser cutting of busbars exposes the cells to possible damages from high heat input (risk of fire and reduced efficiency due to chemical reactions in the cell), molten material and debris. Additionally, the adhesive foils between the cells inhibit the separation of cells without mechanical damaging.

# **5 Discussion of Results**

The proposals for remanufacturing friendly design changes made in this paper are partly adaptable to the design introduced in Fig. 4. Function integration of cooling channels in the components involves the risk of undetectable leaking in the system. Better accessibility requires larger installation space. The same applies to the framed plug-in system for cell grouping. Even though the gaps between cells favour ventilation cooling and provide space for the expansion of cells during usage, the greater need for space is a disadvantage compared to cell connection by means of adhesive foils. Alternative materials for thermal isolation with lower adhesion characteristics are subject of current research in the automotive sector. Without adhesive connections and sufficient accessibility, the exchange of cells directly in the pack could be possible without the removal of modules. By developing alternative contacting methods, measurement technology can be integrated for an inline assessment of the quality of the connection and would replace quality control after contacting. [15] In summary, the challenge in implementing remanufacturing friendly design changes is to avoid interference with classical battery system development goals. New LIB designs must be comparable regarding weight and dimensions in order to offer similar power density as state-of-the-art LIBs.

# **6 Summary and Conclusion**

The analysis of the manufacturing process and used materials and joining technologies results, that the current state-of-the art designs of LIB packs like in Fig. 4 do not allow a disassembly down to cell level. The displayed state-of-the-art LIB-design currently allows the exchange of e-parts on pack level. Mainly the adhesive bonds and non-detachable welding connections inhibit dismantling. This paper suggests design modifications regarding better accessibility of components for disassembly, avoidance of adhesive gap fillers or glue and the use of alternative electrical contacting. High potential lies in the contacting methods laser bonding or micro-clinching. Cooling function integration in components or alternative materials for isolation help to reduce the use of glue and require further research activity.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Author Index**

#### **A**

Arvis, Hélène 68 Avhad, Akshay 133

#### **B**

Bader, Roman 59 Bartels, Jonathan 354 Baumann, Andreas 200 Beck, Joshua 108 Becker, Dina 307 Behrendt, Sebastian 25 Beise, Hans-Peter 179 Berg, Lars F. 307 Birke, Peter 376 Bleifuß, Julian 149 Bormann, Richard 149, 158 Brenner, Carolin 118 Bux, Tobias 230

#### **C**

Carl, Daniel 344 Carosella, Stefan 243 Christ, Michael 210 Colomb, André 118

#### **D**

Diemer, Johannes 324 Doppelbauer, Martin 307 Dorneich, Albert 296

**E** Eisenbart, Boris 243, 266

#### **F**

Fehr, Jörg 200 Fehrenbach, Simon 344 Fill, Alexander 376 Fischer, Marc 3, 35 Fisel, Johannes 25 Fleischer, Jürgen 25 Fox, Bronwyn 253 Frick, Florian 3

#### **G**

Gebauer, Marc 365 Gelgfren, Jan M. 68 Giani, Marco 200 Gönnheimer, Philipp 25 Görzig, David 324 Graner, Melina 376 Gugliuzza, Jakob M. J. 266

#### **H**

Haar, Christoph 344 Hagemann, Simon 68 Hammami, Woidy 376 Heieck, Frieder 376 Holder, Jonas 296 Huber, Marco F. 149, 158 Huse, Timo 314

**I** Imgrund, Christian 190

#### **J**

John, Leonard 307

#### **K**

Kaiser, Manuel 158 Kessler, Daniel 168, 179 Kestler, Hans A. 210 Kley, Markus 59 Klingel, Lars 35 Komesker, Simon 354 König, Timo 59 Koo, Chee H. 46 Kosel, Christian 324 Kraus, Werner 149, 158 Kreimeyer, Matthias 266 Krein, Vincent 253 Kretzschmann, Roman 46 Kuhn, Alexander M. 190, 210 Kuhn, Christopher B. 210

© The Editor(s) (if applicable) and The Author(s) 2023 N. Kiefl et al. (Eds.): SCAP 2022, ARENA2036, pp. 387–388, 2023. https://doi.org/10.1007/978-3-031-27933-1

#### **L**

Landwehr, Inga 344 Lanza, Gisela 25 Larsen, Jørgen S. 85 Leberle, Urs 46 Lechler, Armin 3, 14, 35, 230, 333 Liebgott, Annika 168 Liebgott, Florian 168, 179, 296 Liebl, Michael 266, 296 Lips, Jonas 344 Litty, Katharina 376 Liu, Peng 210 Lorch, Christopher 219 Lüdemann-Ravit, Bernd 219

#### **M**

Madsen, Ole 85, 133 Manns, Martin 78 Martin, Michael 25 May, Marvin C. 25 Middendorf, Peter 243, 253, 266, 296 Mimra, Christopher 243, 253 Mohr, Tobias 296 Möhring, Hans-Christian 307 Moosmann, Marius 149, 158

#### **N**

Neubauer, Michael 35 Neuschwander, Bernd 46

#### **O**

Oechsle, Stefan 3

#### **P**

Paukner, Matthias 333 Pestka, Jonas 96 Pfeifer, Denis 200 Pohlkötter, Fabian J. 190 Puchta, Alexander 25

#### **R**

```
Radjef, Racim 253, 266
Raps, Lukas 282
Regina, David J. 344
Rehberg, Laura 314
Reuter, Steffen 307
Riedel, Oliver 230
Riexinger, Günther 344
Rosport, Johannes 149, 158
```
Rotter, Dominik 168, 179 Ruskowski, Martin 354

#### **S**

Saeed, Muhammad S. 266 Sarivan, Ioan-Matei 85 Sauer, Alexander 344 Scheifele, Christian 200 Schmid-Schirling, Tobias 344 Schnauffer, Georg 324 Schou, Casper 133 Seib, Michael 344 Seitz, Andreas 179 Spenrath, Felix 149, 158 Stade, Dawid 78 Stehle, Patrick 46 Stillig, Javier 118 Straubinger, Dominik 190 Ströbel, Robin 25 Stübing, Julian 344 Stütz, Leon 59

#### **T**

Tangour, Cyrine 365 Tasci, Timur 14, 46 Tekouo, William 190, 210

#### **U** Ulbrich, Sascha 333

### **V**

Verl, Alexander 3, 14, 35, 46, 333 Vorderer, Marian 46

#### **W**

Wæhrens, Brian V. 85 Wagner, Achim 354 Walker, Moritz 3, 14 Weihe, Stefan 96 Wenzel, Sigrid 68 Widmaier, Nils 282 Wnuk, Markus 333

## **Y**

Yang, Bin 168

#### **Z**

Zürn, Manuel 333