可充电锂离子电池因其能量密度高、循环寿命长、成本下降等优点，已广泛应用于从电动汽车到微电网等众多行业的储能领域。充电是锂离子电池补充和储存能量的重要过程，充电策略的好坏极大地影响着锂离子电池的性能和寿命。用精确的数学模型进行分析和预测在充电过程中电池状态的变化，基于先进模型的充电策略可以提供优异的充电性能，如延迟电池寿命的退化。因此，研究基于先进模型的锂离子电池充电控制策略具有重要的工程和学术价值。基于此，本书将从基础理论到实际设计和应用，详细介绍目前*先进的基于模型的锂离子电池充电控制技术，特别是在电池建模、状态估计和*优充电控制方面。此外，还介绍了一些必要的设计考虑因素，如集中式和领导-追随结构的电池组充电控制，为提高充电性能和延长电池/电池组的寿命提供了出色的解决方案。本书所提供的丰富的材料和知识，可以让我们从理论设计到工程应用对电池充电控制技术有足够的了解。

Contents
1 Introduction .................................................. 1
1.1 Brief Introduction of Lithium-Ion Batteries . . . . . . . . . . . . . . . . . . 1
1.1.1 Comparison with Other Commonly Used Batteries . . . . 1
1.1.2 Applications of Lithium-Ion Batteries . . . . . . . . . . . . . . . . 2
1.2 Format Comparison of Lithium-Ion Batteries . . . . . . . . . . . . . . . . . 3
1.3 Electrochemical Mechanism of Lithium-Ion Batteries . . . . . . . . . 6
1.3.1 Composition of Lithium-Ion Batteries . . . . . . . . . . . . . . . 6
1.3.2 Charging-Discharging Mechanism . . . . . . . . . . . . . . . . . . 7
1.4 Motivation of Advanced Model-Based Battery Charging
Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.1 Non-model-based Charging Control . . . . . . . . . . . . . . . . . 10
1.4.2 Model-Based Charging Control . . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Lithium-Ion Battery Charging Technologies: Fundamental
Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1 Definitions Related to Battery Charging . . . . . . . . . . . . . . . . . . . . . 15
2.1.1 Basic Performance Parameters . . . . . . . . . . . . . . . . . . . . . . 15
2.1.2 State Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Charging Objectives and Constraints . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.1 Charging Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.2 Safety-Related Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 22
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3 Lithium-Ion Battery Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1 Electrochemical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.1 Pseudo-Two-Dimensional Model . . . . . . . . . . . . . . . . . . . . 26
3.1.2 One-Dimensional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.3 Single Particle Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Equivalent Circuit Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.1 Rint Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.2 Thevenin Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.3 PNGV Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4 Neural Network-Based State of Charge Observer
for Lithium-Ion Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1 Battery Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Neural Network-Based Nonlinear Observer Design
for SOC Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.1 Neural Network-Based Nonlinear Observer
Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.2 Convergence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3.1 Experiment for Parameter Extraction . . . . . . . . . . . . . . . . 42
4.3.2 Experiments for SOC Estimation . . . . . . . . . . . . . . . . . . . . 45
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5 Co-estimation of State of Charge and Model Parameters
for Lithium–Ion Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.1 Battery Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 Co-estimation of Model Parameters and SOC . . . . . . . . . . . . . . . . 55
5.2.1 On-line Battery Model Parameter Identification . . . . . . . 55
5.2.2 Robust Observer for SOC Estimation . . . . . . . . . . . . . . . . 59
5.2.3 Summary of the Overall SOC Estimation Strategy . . . . . 62
5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.3.1 Experimental Results for Battery Model Parameter
On-line Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.2 Experimental Results for SOC Estimation . . . . . . . . . . . . 68
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6 User-Involved Battery Charging Control with Economic Cost
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.1 Battery Model and Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.1.1 Battery Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.1.2 Safety-Related Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.2 Charging Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.2.1 User-Involved Charging Task . . . . . . . . . . . . . . . . . . . . . . . 80
6.2.2 Economic Cost Optimization . . . . . . . . . . . . . . . . . . . . . . . 80
6.2.3 Energy Loss Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.2.4 Multi-objective Formulation . . . . . . . . . . . . . . . . . . . . . . . . 81
6.3 Optimal Battery Charging Control Design . . . . . . . . . . . . . . . . . . . 82
6.3.1 Optimal Charging Control Algorithm . . . . . . . . . . . . . . . . 83
6.3.2 Optimal Charging Current Determined by Barrier
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.4.1 Charging Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.4.2 Comparison with Other Commonly Used
Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.4.3 Comparison with Charging Control Strategy
without Economic Cost Optimization . . . . . . . . . . . . . . . . 86
6.4.4 Comparison with Charging Control Strategy
Without Energy Loss Optimization . . . . . . . . . . . . . . . . . . 88
6.4.5 Simulation Results for Different Weight Selections . . . . 88
6.4.6 Simulation Results for Different User Demands . . . . . . . 89
6.4.7 Comparison with Traditional CC-CV Charging
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
7 Charging Analysis for Lithium-Ion Battery Packs . . . . . . . . . . . . . . . . 101
7.1 Cell Equalization Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.2 Multi-module Battery Pack Charger . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.2.1 Model and Control of Battery Pack Charger . . . . . . . . . . 103
7.2.2 Performance Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7.3 Battery Pack Charging System Combining Traditional
Charger and Equalizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7.3.1 Classification of Equalization Systems . . . . . . . . . . . . . . . 107
7.3.2 Bidirectional Modified C？k Converter-Based
Equalizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.3.3 Modified Isolated Bidirectional Buck-Boost
Converter-Based Equalizer . . . . . . . . . . . . . . . . . . . . . . . . . 115
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
8 User-Involved Charging Control for Battery Packs:
Centralized Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
8.1 Battery Pack Model and Constraints . . . . . . . . . . . . . . . . . . . . . . . . 121
8.1.1 Battery Pack Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
8.1.2 Charging Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
8.2 User-Involved Charging Control Design for Battery Packs . . . . . . 123
8.2.1 Charging Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
8.2.2 Optimal Battery Pack Charging Control Design . . . . . . . 126
8.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
. . . . . . . . .

Under the background of increasing energy demand and serious environmental crisis (as illustrated in Fig. 1.1), the world is shifting from fossil fuels to renewable energy sources. The renewable energy industries, such as wind energy and photovoltaic, have ushered in opportunities for leapfrog development. Renewable energy is green and low-carbon, which can effectively contribute to energy structure optimization, ecological environment protection, and sustainable economic and social development. But it brings an urgent problem, i.e., how to store and use the generated renewable energy efficiently. Electrochemical energy storage technologies represented by rechargeable batteries have become the most popular energy storage solution [1] since they can directly store and release electrical energy without being restricted by the geographical and terrain environment.

Preface
Rechargeable lithium-ion batteries have been widely used for energy storage in numerous industries stretching from electric vehicles to microgrids due to their advantages of high energy density, long cycle life, and declining costs. Charging is an important process for lithium-ion batteries to replenish and store energy, and the quality of the charging strategy greatly affects the performance and lifetime of lithium-ion batteries. With accurate mathematical models to analyse and predict the changes of the battery’s states during the charging process, advanced model-based charging strategies can provide excellent charging performance, such as delaying the degradation of battery life. Therefore, it is of great engineering and academic value to research advanced model-based lithium-ion battery charging control strategies.
Motivated by this, this book will introduce the state-of-the-art advanced modelbased lithium-ion battery charging control technologies from the fundamental theories to practical designs and applications, especially in terms of battery modeling, state estimation, and optimal charging control. In addition, some other necessary design considerations, such as battery pack charging control with centralized and leader-followers structures, are also introduced to provide excellent solutions for improving the charging performance and extending the lifetime of the batteries/battery packs. The rich materials and knowledge presented in this book
can give sufficient insight into the battery charging control technologies from the theoretical design to engineering applications. This brief is mainly divided into three parts and its organizational structure is as follows:
？ The first part (Chaps. 1 to 3) of this book is devoted to providing an overview of the classifications and chemistry mechanisms of lithium-ion batteries. The fundamental conception of advanced model-based battery charging control and commonly utilized battery models are also described.
？ The second part (Chaps. 4 and 5) aims to introduce two observers to provide the accurate estimated state of charge required in the model-based battery charging control strategies.
？ The third part (Chap. 6) is concerned with a user-involved battery charging control strategy with economic cost optimization.
？ The fourth part (Chaps. 8 to 10) firstly analyses the charging problem of battery packs and then introduce some advanced model-based charging control technologies for battery packs, including user-involved charging control strategies with centralized and leader-followers structures and fast charging control for battery packs.
？ Finally, some future trends of battery charging management are mentioned in Chap. 11.
This work was supported by the National Natural Science Foundation of China (No. 61903189), the China Postdoctoral Science Foundation (No.2020M681589), and the Key Research and Development Program of Zhejiang Province, China (No. 2021C01098).
Nanjing, China
Hangzhou, China
May 2022
Quan Ouyang
Jian Chen