Advances in Battery Manufacturing, Service, and Management Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Series on Systems Science and Engineering
• Sprache: Englisch

Seitenzahl: 616

Veröffentlichungsjahr: 2016

Beschreibung

Addresses the methodology and theoretical foundation of battery manufacturing, service and management systems (BM²S²), and discusses the issues and challenges in these areas

This book brings together experts in the field to highlight the cutting edge research advances in BM²S² and to promote an innovative integrated research framework responding to the challenges. There are three major parts included in this book: manufacturing, service, and management. The first part focuses on battery manufacturing systems, including modeling, analysis, design and control, as well as economic and risk analyses.  The second part focuses on information technology’s impact on service systems, such as data-driven reliability modeling, failure prognosis, and service decision making methodologies for battery services. The third part addresses battery management systems (BMS) for control and optimization of battery cells, operations, and hybrid storage systems to ensure overall performance and safety, as well as EV management.  The contributors consist of experts from universities, industry research centers, and government agency. In addition, this book:

• Provides comprehensive overviews of lithium-ion battery and battery electrical vehicle manufacturing, as well as economic returns and government support
• Introduces integrated models for quality propagation and productivity improvement, as well as indicators for bottleneck identification and mitigation in battery manufacturing
• Covers models and diagnosis algorithms for battery SOC and SOH estimation, data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH
• Presents mathematical models and novel structure of battery equalizers in battery management systems (BMS)
• Reviews the state of the art of battery, supercapacitor, and battery-supercapacitor hybrid energy storage systems (HESSs) for advanced electric vehicle applications

Advances in Battery Manufacturing, Services, and Management Systems is written for researchers and engineers working on battery manufacturing, service, operations, logistics, and management. It can also serve as a reference for senior undergraduate and graduate students interested in BM²S².

Leseprobe

CONTENTS

Cover

Series Page

Title Page

Copyright

Preface

Contributors

Part I: Battery Manufacturing Systems

Chapter 1: Lithium-Ion Battery Manufacturing for Electric Vehicles: A Contemporary Overview

1.1 Introduction

1.2 Li-Ion Battery Cells, Modules, and Packs

1.3 Joining Technologies for Batteries

1.4 Battery Manufacturing: The Industrial Landscape

1.5 Conclusions

References

Chapter 2: Improving Battery Manufacturing Through Quality and Productivity Bottleneck Indicators

2.1 Introduction

2.2 Literature Review

2.3 Problem Formulation

2.4 Integrated Quality and Productivity Performance Evaluation

2.5 Bottleneck Analysis

2.6 Conclusions

Acknowledgment

Appendix A: Operators Θ(⋅), Φ1(⋅) and Φ2(⋅)

References

Chapter 3: Event-Based Modeling for Battery Manufacturing Systems Using Sensor Data

3.1 Introduction

3.2 Sensor Networks for Battery Manufacturing System

3.3 Event-Based Modeling Approach

3.4 Event-Based Diagnosis For Market Demand–Driven Battery Manufacturing

3.5 Event-Based Costing for Market Demand–Driven Battery Manufacturing System

3.6 Conclusions

Acknowledgment

References

Chapter 4: A Review on End-of-Life Battery Management: Challenges, Modeling, and Solution Methods

4.1 Introduction

4.2 Research Issues of Battery Remanufacturing

4.3 Modeling and Analysis for Battery-Remanufacturing Systems

4.4 Summary

References

Chapter 5: An Analytics Approach for Incorporating Market Demand into Production Design and Operations Optimization

5.1 Introduction

5.2 Design and Operational Decision Support

5.3 Linkage to a Financial Transfer Function

5.4 A Quantification of Risk in Design and Operations

5.5 Exploration of Design and Operations Choices

5.6 Manufacturing Operations Transfer Function: Throughput, Inventory, Expense, and Fulfillment

5.7 Activity- Based Costing

5.8 Conclusion

References

Part II: Battery Service Systems

Chapter 6: Prognostic Classification Problem in Battery Health Management

6.1 Introduction

6.2 Failure Predictions by Logistic Regression and JPM

6.3 Numerical Study

6.4 Discussion of the Impact of Imbalanced Data

6.5 Conclusion

References

Chapter 7: A Bayesian Approach to Battery Prognostics and Health Management

7.1 Introduction

7.2 Background

7.3 Battery Model for a Bayesian Approach

7.4 Particle Filtering Framework for State Tracking and Prediction

7.5 Battery Model Considerations for PF Performance

7.6 Decision Making for Optimizing Battery Use

7.7 Summary

References

Chapter 8: Recent Research on Battery Diagnostics, Prognostics, and Uncertainty Management

8.1 Introduction

8.2 Battery Diagnostics

8.3 Battery Prognostics

8.4 Uncertainty Management

8.5 Summary

References

Chapter 9: Lithium-Ion Battery Remaining Useful Life Estimation Based on Ensemble Learning with LS-SVM Algorithm

9.1 Introduction

9.2 LS-SVM Algorithm

9.3 LS-SVM Ensemble Learning Algorithm

9.4 Experiment Verification and Analysis

9.5 Conclusion

References

Chapter 10: Data-Driven Prognostics for Batteries Subject to Hard Failure

10.1 Introduction

10.2 The Prognostic Model

10.3 Simulation Study

10.4 Summary

References

Part III: Battery Management Systems (BMS)

Chapter 11: Review of Battery Equalizers and Introduction to the Integrated Building Block Design of Distributed BMS

11.1 Concept of Battery Equalization

11.2 Equalization Methods

11.3 Introduction of Integrated Building Block Design of a Distributed BMS

11.4 The Proposed Integrated Building Block Design of BMS

11.5 System Implementation

11.6 Tested System Description

11.7 Functional Performance Evaluation

11.8 Conclusion

References

Chapter 12: Mathematical Modeling, Performance Analysis and Control of Battery Equalization Systems: Review and Recent Developments

12.1 Introduction

12.2 Modeling of Battery Equalization Systems

12.3 Performance Evaluation of Battery Equalization Systems

12.4 Control Strategies for Battery Equalization Systems

12.5 Summary

References

Chapter 13: Review of Structures and Control of Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicles

13.1 Introduction

13.2 Batteries for EVs

13.3 Supercapacitors for EVs

13.4 Battery-Supercapacitor Hybrid Energy Storage System

13.5 Control Strategy for HESS

13.6 Conclusions

References

Chapter 14: Power Management Control Strategy of Battery-Supercapacitor Hybrid Energy Storage System Used in Electric Vehicles

14.1 Introduction

14.2 Low-Level Hybrid Topologies

14.3 High-Level Supervisory Control

14.4 Conclusions

References

Chapter 15: Federal and State Incentives Heighten Consumer Interest in Electric Vehicles

15.1 Introduction

15.2 Electric Vehicles and the Federal Role

15.3 Public Interest in HEVs and Electric Vehicles

15.4 Federal Support for HEVs and Electric Vehicles

15.5 Support for EVs in the Obama Administration

15.6 Impact of GHG Regulations

15.7 Vehicle Environmental Life Cycle Comparisons

15.8 State Initiatives

15.9 Prospects for Growth

15.10 Conclusion

Acknowledgment

References

Index

IEEE Press Series on Systems Science and Engineering

End User License Agreement

List of Tables

Table 1.1

Table 1.2

Table 2.1

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Figure 2.1

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Figure 3.6

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Figure 4.1

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Figure 5.1

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Figure 6.1

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Figure 6.9

Figure 7.1

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Figure 8.15

Figure 9.1

Figure 9.2

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Figure 9.4

Figure 9.5

Figure 9.6

Figure 10.1

Figure 10.2

Figure 10.3

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Figure 10.5

Figure 10.6

Figure 10.7

Figure 10.8

Figure 11.1

Figure 11.2

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Figure 11.7

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Figure 11.21

Figure 11.22

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Figure 11.24

Figure 12.1

Figure 12.2

Figure 12.3

Figure 12.4

Figure 12.5

Figure 13.1

Figure 13.2

Figure 13.3

Figure 13.4

Figure 13.5

Figure 13.6

Figure 13.7

Figure 13.8

Figure 14.1

Figure 14.2

Figure 14.3

Figure 14.4

Figure 14.5

Figure 14.6

Figure 14.7

Figure 14.8

Figure 14.9

Figure 14.10

Figure 14.11

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Figure 14.18

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Figure 14.24

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Figure 14.26

Figure 14.27

Figure 14.28

Figure 14.29

Figure 14.30

Figure 14.31

Figure 14.32

Figure 14.33

Figure 14.34

Figure 14.35

Figure 14.36

Figure 14.37

Figure 14.38

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Figure 14.40

Figure 14.41

Figure 14.42

Figure 14.43

Figure 14.44

Figure 14.45

Figure 14.46

Figure 15.1

Figure 15.2

Guide

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardTariq Samad, Editor in Chief

George W. ArnoldXiaoou LiRay PerezGiancarlo FortinoVladimir LumelskyLinda ShaferDmitry GoldgofPui-In MakZidong WangEkram HossainJeffrey NanzerMengChu Zhou

Kenneth Moore, Director of IEEE Book and Information Services (BIS)

Advances in Battery Manufacturing, Service, and Management Systems

Edited by

Jingshan Li

Shiyu Zhou

Yehui Han

Copyright © 2017 by The Institute of Electrical and Electronics Engineers, Inc.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. All rights reserved

Published simultaneously in Canada

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ISBN: 978-1-119-05649-2

Preface

Due to the fast development of mobile technology and renewable energy sources, battery, as a key energy storage device, has played an ever-growing important role in daily life and the world economy. Because of the unprecedent broad application of batteries, the commercial-scale manufacturing of batteries, as well as their service and management, are essential to ensure high quality, high reliability, and high performance of batteries. At present, major investment in battery-related research is on the new battery materials and design. However, the innovative system-level technologies on battery mass production, service, and management are also critical in order to materialize the impact of the emerging battery technology on the society and the economy at a large scale. Furthermore, the growing demands and rapid development in energy storages have introduced enormous challenges in battery manufacturing, service, and management. Therefore, there is a need to systematically address the methodology and theoretical foundation to study the issues and challenges in these areas.

This book volume is exclusively devoted to battery manufacturing, service, and management systems (BM2S2). It highlights the cutting edge research advances in BM2S2 and promotes an innovative integrated research framework responding to the challenges. There are three major parts included in this volume: manufacturing; service; and management.

The first part focuses on battery manufacturing systems, including modeling, analysis, design, and control, as well as economic and risk analyses.

Chapter 1 by Cai presents a comprehensive overview of lithium-ion battery and battery electric vehicle (BEV) manufacturing, with particular emphasis on the joining, assembly, and packaging of battery cells, modules, and packs. The reviews cover from different battery types, advantage and disadvantages of several battery-joining technologies, to battery manufacturing process, as well as applications from battery suppliers and BEV manufacturers.

Chapter 2 by Ju, Li, Xiao, Huang, Arinez, Biller and Deng introduces an integrated model of productivity and quality in serial production lines with repair for battery manufacturing. An analytical method to evaluate line performance is developed and identification methods for downtime and quality bottlenecks are introduced. Such a work can lead to providing a quantitative tool for production engineers to analyze and improve system performance in battery manufacturing.

Chapter 3 by Chang, Li, Biller, and Xiao presents an event-based modeling (EBM) approach for the large-format power battery manufacturing systems. Through quantifying the impacts of disruption events using sensor data, and modeling market demand as an end-of-line virtual station, the whole battery production line is analyzed, and bottleneck-based improvement strategies are proposed. Finally, real-time costing method is established based on EBM.

Chapter 4 by Jin discusses the economic and ecological benefits, principles, operational strategy and processes of battery remanufacturing. In addition, three major challenges associated with batteries remanufacturing, as well as the modeling methods and analytical solutions to address these challenges are discussed, including remaining useful life estimation of end of life (EOL) batteries; decision making for EOL batteries with uncertainty; and remanufacturing operations considering balancing and compatibility issues.

Chapter 5 by Johnson, Biller, Wang, and Biller discusses a set of analytical methods with the capability of raising the economic return while reducing the financial and operational risks in the design and operations of manufacturing ecosystem, built on battery production example at General Electric. The market demand is endogenously incorporated into production design and operations optimization, leading to higher economic returns, robust order fulfillment, and derisked management of capital-intensive projects.

The second part emphasizes on information technology's impact on service system, such as data-driven reliability modeling, failure prognosis, and decision-making methodologies for battery services.

Chapter 6 by Son, Kontar, and Zhou discusses prognosis problem in battery health management in the form of classification problem. It is shown that the joint prognostic model (JPM) outperforms the conventional logistic regression. In addition, a brief discussion on the imbalanced data issue is presented, and a possible resolution for overcoming the issue is provided by oversampling the minority class (failed batteries) or to undersampling the majority class (censored batteries).

Chapter 7 by Saha reviews the conventional approaches used in estimating battery state of charge (SOC) and state of health (SOH), as well as a more advanced algorithmic approach based on Bayesian inference. Both data-driven and model-based approaches are discussed with suggestions on how to leverage the best features of both.

Chapter 8 by Xi, Jing, Lee, and Hayrapetyan investigates recent research on battery diagnostics, prognostics, and uncertainty management especially for lithium-ion (Li-ion) batteries, such as models and diagnosis algorithms for battery SOC and SOH estimation, data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH, and management of five important types of uncertainties, which play key roles for reliable battery diagnostics and prognostics.

Chapter 9 by Peng, Lu, Xie, Liu, and Liao compares several popular model aggregation techniques and presents a new alternative for lithium-ion battery prognostics. The method utilizes the mixture of probability distributions obtained from different RUL estimation models and updates the aggregation structure under a Bayesian framework. It is also extendable to more complex data-driven prognostic applications where heterogeneous models are developed.

Chapter 10 by Zhou, Man, and Son introduces a prognostic framework for estimating the RUL of batteries under hard failure. A joint modeling scheme is used to take into consideration both the degradation data and the time-to-failure data. The maximum power interval (MPI) is introduced to better assess the performance of the prognostic algorithm. Such an algorithm is suitable for applications where monitored degradation signal (e.g., internal resistance) does not have a clear preset failure threshold.

The third part addressing battery management system (BMS) intends to control and optimize battery cells, operations, and hybrid storage systems to ensure overall performance and safety, as well as EV management.

Chapter 11 by Li, Han, and Zhang reviews and compares the topologies, advantages, and disadvantages of different kinds of battery equalizers and introduces their broad applications. In addition, an integrated building block design of a distributed BMS is presented, which integrates the power electronics and the battery cell together in each building block to achieve easy reconfiguration and installation, high equalization efficiency and speed, and enhanced protection and reliability.

Since battery equalization is a critical part of the BMS, Chapter 12 by Han, Zhang, and Han presents an overview of the results to date on the issues of mathematical modeling, performance analysis, and control of battery equalization systems. In addition, the new developments in this area are discussed and the future topics are summarized.

As batteries have high energy density and relatively low cost, but low specific power and short cycle life, while supercapacitors preserve high peak power, long cycle life, but relatively low energy density and high cost, combining the two can potentially overcome the drawbacks of each single energy storage device. Chapter 13 by Ju, Zhang, Deng, and Li reviews the state of the art of battery, supercapacitor, and battery-supercapacitor hybrid energy storage system (HESS) for advanced electric vehicle applications, and discusses the optimal control methods for such HESS.

Following it, Chapter 14 by Zhang, Deng, Wu, Ju, and Li studies power management strategies for HESS in electric vehicles. The goal is to improve energy supply and power flow for better vehicle performance, energy efficiency, and extended battery life. The low-level hybrid topologies are reviewed, and high-level supervisory control methods are discussed. An integrated power management strategy to combine both time and frequency domain controls is presented.

Chapter 15 by Canis discusses the steps by which federal support for battery research has grown over the past 40 years and considers how successful the major initiatives have been in promoting development and use of electric vehicles. It also reviews some of the major battery technologies in use and on the drawing board and the framework of international competition that is shaping this infant industry.

The editors are grateful to Mr. Cong Zhao of University of Wisconsin-Madison for his substantial efforts on organizing, editing, and reviewing of the chapters, and the anonymous reviewers for their helpful comments to improve the book quality. The funding support from the Research Innovation Committee at College of Engineering in University of Wisconsin-Madison is invaluable to the success of this work. In addition, we express our deep gratitude to Mary Hatcher, Brady Chin, and Allison McGinniss of Wiley and Dr. Mengchu Zhou, Series Editor, who have provided incredible support to this book volume.

Jingshan Li

Shiyu Zhou

Yehui Han

Contributors

Jorge Arinez, Manufacturing Systems Research Lab, General Motors Research & Development Center, Warren, MI, USA

Bahar Biller, General Electric Global Research Center, Niskayuna, NY, USA

Stephan Biller, GE Global Research Center, General Electric, Niskayuna, NY, USA

Wayne Cai, Manufacturing Systems Research Laboratory, General Motors Global, R&D Center, Warren, MI, USA

William Canis, Congressional Research Service (CRS), The Library of Congress, Washington, DC, USA

Qing Chang, Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY, USA

Weiwen Deng, State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China

Weiji Han, Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA

Yehui Han, Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA

Mushegh Hayrapetyan, University of Michigan—Dearborn, MI, USA

Ningjian Huang, Manufacturing Systems Research Lab, General Motors Research & Development Center, Warren, MI, USA

Xiaoning Jin, University of Northeastern, Boston, MA, USA

Rong Jing, University of Michigan—Dearborn, MI, USA

Chris Johnson, General Electric Global Research Center, Niskayuna, NY, USA

Feng Ju, Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI, USA; and State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China

Raed Kontar, Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA

Cheol Lee, University of Michigan—Dearborn, Dearborn, MI, USA

Jingshan Li, Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI, USA; and State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China

Yang Li, School of Mechanical Engineering, Tongji University, Shanghai, China

Ye Li, Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA

Haitao Liao, University of Arizona, Tucson, AZ, USA

Datong Liu, Harbin Institute of Technology, Harbin, P. R. China

Siyuan Lu, Harbin Institute of Technology, Harbin, 150080, P. R. China

Jianing Man, Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong

Yu Peng, Harbin Institute of Technology, Harbin, P. R. China

Bhaskar Saha, Palo Alto Research Center, Palo Alto, CA, USA

Junbo Son, Alfred Lemer College of Business and Economics, University of Delaware, Newark, DE, USA

Shanshan Wang, General Electric Global Research Center, Niskayuna, NY, USA

Jian Wu, State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China

Zhimin Xi, University of Michigan, Dearborn, MI, USA

Wei Xie, University of Arizona, Tucson, AZ, USA

Guoxian Xiao, Manufacturing Systems Research Lab, General Motors Research & Development Center, Warren, USA and General Motors R&D, General Motors Corporation, Warren, MI, USA

Liang Zhang, Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA

Qiao Zhang, State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China

Shiyu Zhou, Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA

Qiang Zhou, Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong

Part IBattery Manufacturing Systems

Chapter 1Lithium-Ion Battery Manufacturing for Electric Vehicles: A Contemporary Overview

Wayne Cai

Manufacturing Systems Research Laboratory, General Motors Global R&D Center, Warren, MI, USA

1.1 Introduction

During the last few decades, environmental concern about the petroleum-based transportation has led to renewed and stronger interest in electric vehicles (EV). In an EV, energy storage devices (such as batteries, supercapacitors) or conversion devices (such as fuel cells) are used to store or generate electricity to power the vehicle. The first highway-capable EV with mass production in the modern age was GM's EV1 [1], which used lead-acid-based batteries as onboard energy storage. With the advancement of newer generations of high-density energy storage batteries such as the metal-hydride batteries and most recently the lithium-ion (Li-ion) batteries, battery electric vehicles (BEVs) have seen tremendous growth in the past decade. Batteries used as the power and energy sources to drive BEVs are called traction batteries.

A BEV falls into one of the following four categories: hybrid electric vehicle (HEV), plug-in electric vehicle (PHEV), extended range electric vehicle (EREV), and pure BEV. An HEV is generally powered by an internal combustion engine and a battery pack. The internal combustion engine is the primary source of energy during medium or high-speed driving conditions with the batteries serving as the main power source in stop-and-go traffic as well as power assist in vehicle acceleration, where the batteries are also called power batteries. The battery pack is relatively small and recharged by the internal combustion engine and regenerative braking. An exemplary vehicle is Toyota's Prius (2015 model year), offering an EPA-estimated 50 mpg fuel economy using a small 4.3 kWh of Li-ion battery pack [2]. A PHEV operates under either the battery mode, the internal combustion engine mode, or a combination of the two modes. The battery pack, however, can be charged via an external electrical power grid. An exemplary vehicle is Toyota's Prius Plug-in [2]. Depending on the design intent and the size of the battery pack, the traction batteries in PHEV can be either power or energy batteries. An EREV differs from a PHEV in that the battery pack is relatively large and the vehicle operates primarily under the electric mode. The internal combustion engine in the vehicle is used exclusively or primarily to charge the traction batteries (although the internal combustion engine can also be used to assist the battery mode driving in special circumstances). An exemplary vehicle is GM's Chevrolet Volt [3]. A pure BEV is powered entirely electrically by an onboard battery pack through the traction motors. The battery pack is typically recharged via an external electrical power grid. Although many automakers are mass-producing BEVs in the marketplace, the most notable models are Tesla Model S [4], Nissan LEAF [5], and BMW i3 [6,7]. Figure 1.1 shows a complete landscape of major BEV manufacturers and their Li-ion battery cell suppliers. A comparison with reference [8] immediately finds significant industry evolution during the past 5 years. While every single global automotive manufacturer is now producing BEVs, a few BEV start-ups and many traction battery joint ventures have been reorganized or even gone out of business during the past few years. At the end of 2014, Panasonic, AESC, LG Chem, and BYD were the top four largest manufacturers of traction battery cell in the world, supplying batteries to Tesla Model S (pure BEV), Nissan LEAF (pure BEV), GM Chevrolet (EREV), and BYD (pure EV and PHEV), among others [9]. Table 1.1 lists some key technical data for several major BEVs in the marketplace.

Figure 1.1 Major BEV manufacturers and Li-ion battery suppliers (2015 landscape).

Table 1.1 Selected technical data for major BEVs

Toyota Prius Plug-in [2]

GM Chevy Volt [3]

Tesla Model S [4]

Nissan LEAF [5]

BMW i3 [6,7]

Model Year

2012–2015

2015

2012–2015

2013/2014

2014

Energy Storage (kWh)

4.4

16

85

24

18.8

Fuel Economy (MPGe)

58

62

89

115

124

Pure Electric Driving Range (miles)

11

39

265

75

81

Cell Manufacturer

Panasonic

LG Chem

Panasonic

AESC

Samsung SDI

# of Cells

56

288

7104

192

96

Cell Format

Prismatic

Pouch

Cylindrical

Pouch

Prismatic

Cell-to-Cell Joining

Bolting

Ultrasonic welding

Wire bonding

Ultrasonic welding

Laser welding

# of Modules

3

9

16

48

8

Module-to-Module Joining

Bolting

1.2 Li-Ion Battery Cells, Modules, and Packs

This section reviews different formats and structures of Li-ion battery cells, modules, and packs as seen in BEVs. The focus is on the characteristics relevant to the joining, assembly, and packaging rather than the battery chemistries, functions, and performances.

1.2.1 Formats of Li-Ion Battery Cells

A battery cell is the most basic and fully independent operating unit in a storage battery. It is primarily composed of positive and negative electrodes, separators, electrolytes, and a container. In the current marketplace, there exist primarily three different cell formats for a traction battery cell: cylindrical, prismatic, and pouch. Due to legacy reasons, the cylindrical format has been the mainstream ranging from alkaline (such as AA cells) to NiMH to Li-ion (such as 18650) cells. However, when rechargeable batteries such as NiMH or lithium-ion batteries have been considered for automotive battery applications, other formats of battery cells such as prismatic and pouch types have been developed to improve the volumetric efficiency [10], accommodate thermal management, and/or packaging requirement.

1.2.1.1 Cylindrical Cells

Figure 1.2a shows an 18650 cylindrical cell (i.e., 18 mm in diameter and 65 mm in height) as used in Tesla's EVs. Figure 1.2b is an anatomy of a representative cylindrical cell [11], although the exact structure varies for different manufacturers. The cylindrical cells are known to be less volumetric efficient than prismatic cells. However, the commercial production lines for many cylindrical cells allow for some advanced passive safety features such as positive temperature coefficient (PTC) and current interrupt device (CID) (Figure 1.2c and d) to be built in reference [12]. PTC is a type of material that demonstrates significantly high electrical resistivity at high temperatures so as to melt the PTC itself to break the circuit at higher electrical current [13]. CID is another passive device that breaks when the pressure inside a cell reaches high levels. Normally, when the cell is overheated, such as in a thermal runaway, the pressure increases to a level to break the CID and thereof the circuit [13]. Although manufacturers make cylindrical cells of many different dimensions, 18650 cells are the most produced cells due in part to Tesla's needs.

Figure 1.2 Cylindrical format Li-ion battery cells.

1.2.1.2 Prismatic Cells

For a prismatic Li-ion battery cell, the current collectors (after anode and cathode coatings) and separators are either wound, as shown in Figure 1.3 [14], or laminated (not shown), and then inserted into a prismatic shaped container and sealed all together after filling in the electrolyte. The container is normally made of steels or aluminum alloys (although can be plastics too) and offers rigidity for dimensioning, handling, and protecting the cell. No standard exists as to the size of prismatic cells.

Figure 1.3 An anatomy of a prismatic Li-ion battery cell. (Reproduced from Ref. [14] with permission from Cadex Electronics, Inc.)

1.2.1.3 Pouch Cells

A number of BEV manufactures use pouch cells for light-weighting, better volumetric energy density, and high spatial efficiency. Inside the cells (Figure 1.4) [15], multiple layers of precut positive/negative electrodes and separators are stacked with electrode leads (or tabs) and then welded. Then the edges of the aluminum-laminated films (i.e., the pouch materials) are heat sealed. Similar to prismatic cells, no standards exist as to the size of pouch cells.

Figure 1.4 Schematics of pouch-type cells. (Reproduced from Ref. [15] with permission from Automotive Energy Supply Corporation.)

1.2.2 Battery Modules and Pack

A module is a group of two or more battery cells joined together that can be replaced in maintenance and repair without impacting the rest of the battery pack. A module is also typically the minimum unit installed with safety components, power and heat management electronics. Modules can vary in their sizes, see Table 1.1. A pack is a collection of all battery modules in the BEVs. The enclosure of a battery pack is sealed and watertight; it can protect the modules inside in the event of vehicle impact or crash. Due to excessive flexibility and softness of the pouch cells, holding components are generally needed to prevent pouch cells from having dimension and alignment issues. Such holding components can include frames, rigid cases, and supporting trays.

1.3 Joining Technologies for Batteries

This section reviews a few selected joining technologies that are most pertinent to Li-ion battery cell and pack manufacturing, that is, ultrasonic welding, resistance welding, laser welding, wire bonding, and mechanical joining. The advantages and limitations are discussed. The applications in major BEVs will be discussed in Section 1.4.

1.3.1 Ultrasonic Metal Welding

1.3.1.1 Overview

Ultrasonic metal welding (USMW) is a process in which a high frequency, usually 20 kHz or above, of ultrasonic energy is used to produce relative lateral motions to create solid-state bonds between two or multiple metal sheets clamped under pressure. The high-frequency shear forces induce alternating metal surface friction and heat to produce a weld. A schematic of the USMW system is shown in Figure 1.5 [16]. It can be used to join a wide range of metal sheets or thin foils, see Figure 1.6 [17].

Figure 1.5 A schematic of USMW system. (Reproduced from Ref. [16] with permission from Cadex Electronics, Inc.)

Figure 1.6 (a) Aluminum wire welding onto copper. (b) Copper and aluminum welding. (c) Multilayered copper foil welding. (Reproduced from Ref. [17] with permission from Nippon Avionics Co., Ltd.)

USMW is considered a solid-state welding. In contrast to fusion welding processes, USMW has several inherent advantages. The main advantage of USMW lies in its excellent welding quality for thin, dissimilar, and multiple layers of highly conductive metals (such as Cu and Al), which is crucial in battery cell joining and for battery tab joining [18]. Another advantage is the low heat-affected zone. USMW also produces a very thin layer of bonding interfaces (typically a few microns) and therefore eliminates metallurgical defects that commonly exist in most fusion welds such as porosity, hot-cracking, and bulk intermetallic compounds. Therefore, it is often considered the best welding process for Li-ion battery applications.

1.3.1.2 Ultrasonic Welding Physics

The bonding mechanisms for USMW are not completely understood. A combination of the following four mechanisms may attribute to the bonding: (a) micromelting (e.g., a few microns of thin interface layer melting), (b) metal interlocking (due to plastic deformation, particularly the severe deformation caused by sonotrode knurls), (c) diffusion, and (d) chemical bonding (such as covalent and metallic bonding). To better understand different phases generated at the bonding interfaces, an Al-Cu phase diagram such as the one in reference [19] should be consulted.

Figure 1.7 [20] illustrates how the joints of multiple metal sheets are generated in ultrasonic welding. It is believed that the propagation of the welds is through the top layer (i.e., the layer in contact with the sonotrode) to the bottom layer (i.e., the layer in contact with the anvil) [20].

Figure 1.7 Weld propagation in USMW of multiple sheets.

There are three important physical attributes pertaining to the quality of an ultrasonic weld:

The temperatures at the bonding interfaces.

Recent researches [21,22] were able to measure in situ the welding temperatures using the self-developed thin-film thermocouples and thermopile sensors. Although it was found that the measured temperature is 660°C for Cu-Cu ultrasonic welding at 1 mm away for the weld spot, it was uncertain whether melting actually occurred at the weld spots. According to the Fick's laws of diffusion [23], temperature is the most critical factor in diffusion bonding to ensure proper rate of solid-state atomic diffusion between the interfacing metals. Therefore, from both melting and diffusion points of view, higher temperature will be beneficial to the welding quality. In this respect, research has been performed to predict the welding temperature [24]. Higher welding temperatures and more uniform welding temperature distribution can also be achieved via a number of innovative methods shown in Figure 1.8 [24]. According to reference [18], in USMW, if the welding parameters (e.g., vibration amplitude, welding pressure, and welding time) are set too low, low temperatures at the weld interfaces result in low bonding strength.

Fracture and perforation of the metals at the weld spots.

The second attribute critical to the weld quality is the fracture and perforation of the metals. Figure 1.9a is a sketch of a two-layered circular ultrasonic weld whose size is dictated by the sonotrode knurl area as shown in Figure 1.9b. According to reference [18], if the welding parameters (e.g., vibration amplitude, welding pressure, and welding time) are set too high, excessive plastic deformation and/or higher temperatures can result in metal fracture and perforation at the weld spots and consequently poor joint strength, although the interfacial bonding strength can be higher. Figure 1.10 shows micrographs of several ultrasonic welds including a Cu-Cu weld (Figure 1.10a), where the top Cu layer is fractured/perforated fairly severely.

Structural stress and failure induced by USMW.

Since the ultrasonic vibration is a mechanical wave that can propagate to cause stresses throughout the entire system, it is important to ensure that the structure (under specific boundary conditions) does not have a natural frequency at or near the ultrasonic frequency. As an example, paper [27] studied the distribution of stresses in the battery tab during ultrasonic welding using an analytical mechanistic model, as shown in Figure 1.11. The main focus of the study was to assess the effects of the elastic vibration of the battery tab on the stress development near the weld spot area during welding. It was found that the natural frequencies of the tab depend on the length between the weld spot and tab-end (interface between the battery tab and cell pouch), boundary conditions of the tab-end, the cross-sectional area, and the material of the tab. Therefore, it is important to design the weld position on the tab such that the tab's natural frequencies stay away from 20 kHz as much as possible to minimize tab stresses. Further, the stresses near the weld area could exceed the tab material's yield strength and cause fatigue fracture.

For larger and complex structures, modal analyses using finite-element analysis should be performed to extract the natural frequencies of a welding system under different design options in order to optimize the system to move away from resonance at the ultrasonic welding frequency. Further, steady-state dynamics analysis should also be performed to investigate the field outputs such as the stress/strain/displacement under the ultrasonic welding vibration input (such as +/–20 μm of sinusoidal input of 20 kHz) to assess the soundness of the design. As another example, Figure 1.12 depicts one welding configuration on a battery interconnect circuit board, consisting of a positive and a negative battery tab welded with a U-shaped interconnect busbar [28]. Welding of the tabs is performed on one side (i.e., the left-hand side Cu tab welding with the interconnect) followed by the subsequent welding on the other side of the interconnect. The joint made by the first welding is termed as “existing weld,” and the joint made by the subsequent welding action is called “active weld.” The ultrasonic vibration generated during the active weld welding may place enough stresses on the system and destroy the existing weld on the left-hand side [28]. Therefore, the system (including the choice of materials and boundary conditions) should be designed to avoid resonance under ultrasonic welding.

Figure 1.8 Temperature contours at the end of a 500 ms ultrasonic welding for (a) baseline model, (b) insulated anvil, (c) 100°C preheating, and (d) 0.6 mm thick busbar instead of 0.9 mm. (Reproduced from Ref. [24] with permission from ASME.)

Figure 1.9 (a) Two layers of metals with sonotrode knurl marks. (b) A sonotrode (with knurls on the tip).

Figure 1.10 Micrographs of ultrasonic welds.

Figure 1.11 A pouch battery cell assembly (with the pouch partially shown) under ultrasonic vibration. (Reproduced from Ref. [27] with permission from ASME.)

Figure 1.12 Modeling of battery welding system: (top) boundary conditions and (bottom) finite-element mesh.

1.3.1.3 Ultrasonic Welding Quality Evaluation

Standards and guidelines for destructive, postweld quality evaluation are well established [29] for many types of welds, including ultrasonic spot welds [30,31]. They generally prescribe the quality evaluation and testing procedures. As for nondestructive evaluation (NDE), a variety of methods are developed using ultrasonic probes, eddy current, X-ray/CT, electrical resistance, etc. Success of any of the methods largely depends on the nature of the weld and defect types, and significant challenges exist in interpreting the test data. Jia et al. [32] reported that stereography method has the potential for accurate NDE for ultrasonic welds. In terms of real-time, online welding process monitoring and NDE, no standard or guideline exists, although many sensors such as temperature sensors (including thermocouples, infrared (IR)), force/pressure sensors (such as load cells), displacement (such as linear variable differential transformer (LVDT), accelerometers, and acoustic sensors are reportedly used [33]. In particular, online monitoring and quality assurance methods were developed for GM's Chevy Volt [34] and Cadillac ELR [35].

1.3.2 Resistance Welding

Resistance welding relies on a higher contact resistance at the joint interface to induce a localized joule heating and fusion of materials when the electrical current is applied through two electrodes. Resistance welding process is fast and generally automated. It has wide applications in sheet metal industries, particularly for steels welding. Although resistance welding has also been used in the battery welding for decades, its usage was primarily limited to low current-carrying applications and faces challenges in high-power BEVs:

Li-ion battery metals use highly electrically and thermally conductive materials such as aluminum and copper. These metals are difficult to weld using the conventional resistance spot welding technology, particularly when the contact resistance(s) at the metal interface(s) becomes low. It hence requires very large electrical current density (i.e., current vs. weld size) to be applied in the welding circuit to generate enough joule heat at the intended weld interfaces. A steady stream of recent advances has given users much improved capabilities to control various aspects of the process. For example, projection resistance welding method can sometimes be used where a small metal projection is introduced at one of the metals to reduce the total contact area of the metals and hence increase the current density. Another solution is to increase the current density by using a special type of resistance welder called capacitive discharge welder [36] to provide very high welding current (such as 10–100 kA) in a very short period of time (such as 10 ms). Nevertheless, it is still very difficult to produce a large-sized weld nugget for battery metals because of the extremely high electrical current density required.

As is the case for all fusion welding technologies of dissimilar materials, welds are difficult to form due to different melting temperatures; in addition, a large amount of intermetallics is typically produced, making the weld brittle with very high electrical resistivity.

When welding multiple layers, it is very difficult to ensure homogenous melting at all the interfaces.

Figure 1.13 [37] and Figure 1.14 [38] shows the resistance welding/welds using single-side welding electrodes, that is, two electrodes are on the same side of the metals to form a closed current conducting circuit instead of the more conventional process of two-sided resistance welding (not shown) with the two electrodes on each side of the metals. As early as 2004, NASA [37] set process specifications for the resistance spot welding of battery and electronic assemblies, where battery assemblies are considered to be nonstructural without load-carrying requirement.

Figure 1.13 Resistance spot welding of battery and electronic assemblies. (Reproduced from Ref. [37]. Public domain.)

Figure 1.14 Battery cell resistance spot welding by (a) Amada Miyachi (reproduced from Ref. [36] with permission from Amada Miyachi) and (b) Sunstone Engineering (reproduced from Ref. [38] with permission from Sunstone).

1.3.3 Laser Beam Welding

Laser beam welding (LBW), or simply laser welding, is a welding technique to join workpieces through the use of the high power beam of laser. The process has been frequently used in high-volume applications, but recently it has also been used in electronics and battery industries. Figure 1.15 shows laser welding of busbars to cylindrical battery cans [39]. Figure 1.16a shows a laser seam welding of an aluminum can [39]. In battery tab welding, as described in Figure 1.16b, weld penetration must be controlled accurately so that the weld nugget does not penetrate into the can [39].

Figure 1.15 Laser welding of busbars to cylindrical battery cans. (Reproduced with permission from Amada Miyachi.)

Figure 1.16 (a) Laser seam welding of an aluminum can. (b) A nickel battery tab laser welded to a stainless steel casing. (Reproduced with permission from Amada Miyachi.)

Laser welding can offer significant advantages in process precision, throughput, and noncontactness. It also produces a small heat-affected zone, resulting in low weld distortion and low residual stress. Low heat input and low weld penetration can also reduce the adverse effect of heat flux on the structure and the chemistries of battery cells. However, the need of precise joint fit-up and the high reflectivity of the battery materials (e.g., Cu and Al) make laser welding challenging in battery applications. More critically, due to the large amount of intermetallics from the fusion welding process, weld defects such as porosity and hot cracking can be significant [40]. Recent research (Figure 1.17) demonstrated superior joining quality using a laser braze-welding process. This process uses a very small laser beam diameter (such as 50 μm), very high beam quality (i.e., high factor) in conjunction with high-speed beam scanning to precisely control the melting of aluminum layer only so that the Al and Cu welding is essentially a brazing process (without filler materials) [41].

Figure 1.17 Laser braze welding of pouch type of battery tabs with U-shaped busbar. (Reproduced from Ref. [41] with permission from Elsevier.)

1.3.4 Wire Bonding

Wire bonding [42] is a single-sided ultrasonic welding that bonds an auto-fed small diameter (typically 0.01–0.500 mm) Ag, Cu, or Al wire to one substrate first (called the first bond) and then to the second or more substrates in sequence (i.e., the second bond, the third bond, etc.) to establish an interconnect between the substrates through the bonding wire. Wire bonding is widely used in microelectronics industry and generally considered the most cost-effective and flexible interconnect technology. Figure 1.18 is a photo of wire-bonding process [43].

Figure 1.18 Wire bonding: the bond head is finishing the second bond on a Cu substrate. (Reproduced from Ref. [43] with permission from Hesse Mechatronics.)

For high-power applications such as BEVs, heavy gauges of feed wires (either Al or Cu) are needed. For example, Tesla Model S uses 0.381 mm diameter of Al wires as interconnects between its 18650 Li-ion battery cells and the bus plate, as shown in Figure 1.19 [44].

Figure 1.19 Tesla Model S battery joined by wire bonding. (Reproduced from Ref. [44]. Public domain.)

1.3.5 Mechanical Joining

Mechanical joining [45] can be categorized by two distinct groups: fasteners and integral joints. Fasteners include nuts, bolts, screws, pins, and rivets. Integral joints include seams, crimps, snap-fits, and shrink-fits that are designed into the components to be connected. For example, snap-fit is a mechanical joining method in which part-to-part attachment is accomplished with locating and locking features (constraint features) [46]. For battery module-to-module connection, mechanical joining is preferred for the ease of disassembly for maintenance and repair.

1.3.6 Summary

The key characteristics of the selected battery-joining technologies are summarized in Table 1.2.

Table 1.2 Summary of battery-joining technologies

Joining methods

Advantages

Disadvantages

Ultrasonic welding

Excellent for dissimilar materials due to minimal intermetallics

Low heat-affected zone: low thermal distortions and low residual stresses

Excellent for highly conductive materials

Excellent for thin sheets or wires

Excellent for multiple wires or multilayered sheets

Double-sided

May have severe knurl perforation at the top and/or bottom weld surface

May cause structural vibration

Has an upper limit in total joint thickness

Most suitable for soft materials

Resistance welding

Can be single-sided welding

Relatively mature technology with established weld quality monitoring and/or control methods

Low cost

Large heat-affected zone: large thermal distortion and residual stresses

Large amount of intermetallics for dissimilar materials

Difficult for highly conductive materials

Difficult for multiple layers

Difficult to produce large welds

Electrode sticking/wear

Laser welding

Relatively small heat-affected zone: small thermal distortion and residual stresses

Single-sided and noncontact

High throughput

Large amount of intermetallics for dissimilar materials

Porosity and hot-cracking

Requiring very tight sheets fit-up

High initial cost

Wire bonding

Excellent for dissimilar materials due to minimal intermetallics

Low heat-affected zone: low thermal distortions and low residual stresses

Excellent for highly conductive materials

Single-sided

Built-in bond strength testing

Only light gauges of wires can be bonded onto the substrates (such as the busbars or bus plates) and thus the electrical current carrying capability is limited

Most suitable for soft materials

Substrate needs to have rigidity to sustain the bonding force

Mechanical joining

Joint strengths can be very high

Easy disassembly

Added parts and mass

Labor-intensive

Corrosion

1.4 Battery Manufacturing: The Industrial Landscape

This section discusses the battery manufacturing processes and particularly the joining processes. Depending on the design specifics of cells, modules, packs, and the battery management system, the manufacturing processes vary. The section hence focuses on the manufacturing processes most relevant to BEVs in today's marketplace.

1.4.1 Cell Manufacturing

At the high level, Li-ion battery cell manufacturing processes are common for all three different cell formats [47,48], although processes vary according to different cell designs (such as PTC/CID), materials used (such as the container materials: aluminum alloys or steels), and the supplier's design preferences. In particular, the manufacturing processes for cylindrical [49] and prismatic cells are substantially the same but somewhat different for pouch-type cells [50], as shown in Figure 1.20.

Figure 1.20 LG Chem's Li-ion battery cell (pouch-type) manufacturing process. (Reproduced from Ref. [50] with permission from LG Chem.)

The following is a list of battery cell components requiring joining:

For all cell formats

Cathode current collector (i.e., foil):

commercial grade pure Al (e.g., 1100)

Anode current collector (i.e., foil):

commercial grade pure Cu (e.g., CDA 110)

Positive electrode lead (i.e., tab):

commercial grade pure Al (e.g., 1100)

Negative electrode lead (i.e., tab):

commercial grade pure Cu (e.g., CDA 110), or Ni

For cylindrical and prismatic cells only

Enclosure case (i.e., container):

steels, stainless steels, aluminum alloys

Enclosure cover (i.e., top plate):

steels, stainless steels, aluminum alloys

Welding occurs for the following four scenarios in a battery cell:

(For all cell formats): between an electrode lead/tab and multiple (such as 10–100) layers of current collectors [51]. Thickness of each layer ranges from 10 to 30 μm [51] depending on the design and materials used, and the cathode foils are thicker than the anode foils when Al and Cu are used. The thickness of the lead/tab is 0.1–0.2 mm. Ultrasonic welding is commonly used.

(For all cell formats): for multiple layers of foils themselves. This welding operation is optional. Ultrasonic welding is commonly used.

(For cylindrical cells only): between a positive tab and a positive terminal, or a negative tab and the bottom of the enclosure case. Laser welding or resistance spot welding is commonly used.

(For prismatic cells only): between the enclosure case and the cover. Laser welding is commonly used.

1.4.2 Module Assembly (Cell-to-Cell)

A number of battery cells are normally grouped together, either in parallel or series, to form a module. Often, circuitry sensors and safety devices, along with busbars or conduction plates are also joined together with the cell tabs or terminals. The busbars or conduction plates are made of Cu or Al. On the contrary, busbars are usually much thicker than battery cell tabs. Therefore, tab-to-busbar joining is a high-gauge ratio's joining, which may limit the choice of joining method. In addition, for pouch cells, positive battery tabs are typically made of aluminum, while negative tabs are made of copper, thus requiring dissimilar materials joining. Figure 1.19 shows the Tesla Model S pack [44] in which each 18650 cylindrical cell is wire bonded to the Cu bus plate. Figure 1.21 shows a schematic of GM's Chevy Volt battery module (partial view) in which three pouch cells are ultrasonically welded to the Cu busbar for each weld [52]. Figure 1.22 shows a Nissan LEAF battery module consisting of four battery cells [53], two of which are connected in series and two in parallel. BMW i3 battery module design and manufacturing process can be found in reference [54].

Figure 1.21 GM Chevy Volt: a part of battery module under ultrasonic welding.

Figure 1.22 Nissan LEAF's battery module. (Reproduced from Ref. [53] with permission from SAE.)

1.4.3 Pack Assembly (Module-to-Module)

Module-to-module assembly is normally mechanically joined via bolts/nuts with busbars. In fact, welding is not recommended at this stage due to the need of disassembly of battery packs. Figure 1.23 shows Nissan LEAF's battery pack [55].

Figure 1.23 Nissan Leaf battery cell, module, and pack. Upper left: a laminated battery cell; upper right: a battery module set of four laminated battery cells; bottom: a battery pack made up of 48 modules. (Reproduced from Ref. [55] with permission from IEEE.)

1.5 Conclusions

This chapter provides a comprehensive overview on the state-of-the-art of BEV manufacturing, with emphasis on the joining, assembly, and packaging of lithium-ion battery packs.

Li-ion battery and BEV marketplace are growing and evolving rapidly. In 2014, Panasonic, AESC, LG Chem, and BYD were the four largest manufacturers of traction battery cells in the world, supplying batteries to Tesla Model S (pure BEV), Nissan LEAF (pure BEV), GM Chevrolet (EREV), and BYD (pure EV and PHEV), respectively.

There are three major cell formats for Li-ion traction batteries, that is, cylindrical, prismatic, and pouch. The manufacturing processes for cylindrical and prismatic cells are substantially similar but deviate meaningfully for the pouch-type cells. The exact manufacturing process for any format is determined by the designs, materials, and cell and BEV manufacturers' preferences.

The traction Li-ion battery joining is an important manufacturing process at three different levels, that is, cell level (inside cell joining), module level (cell-to-cell joining), and pack level (module-to-module).

Ultrasonic welding, Laser beam welding, resistance welding, wire bonding, and mechanical joining are the commonly used joining techniques for Li-ion battery cells, modules, and packs.

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