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Advanced Battery Management Technologies for Electric Vehicles

Advanced Battery Management Technologies for Electric Vehicles

Rui Xiong, Weixiang Shen

ISBN: 978-1-119-48168-3

Mar 2019

264 pages


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This book will focus on model based state estimation methods, battery charging and balancing techniques for practical engineering applications. It will be presented in a way to fit the scope of a textbook which is suitable for senior undergraduate students, postgraduate students, researchers, engineers and self-learners, leading readers from fundamental into the advanced topics.

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Chapter 1 Introduction

1.1. Background

1.2. Electric vehicle fundamentals

1.3. Requirements for battery systems in electric vehicles

1.3.1. Range per charge

1.3.2. Acceleration rate

1.3.3. Maximum speed

1.4. Battery systems

1.4.1. Introduction to electrochemistry of battery cells

1.4.2. Lead acid batteries

1.4.3. NiCd and NiMH batteries

1.4.4. Lithium ion batteries

1.4.5. Battery performance comparison

1.5. Key battery management technologies

1.6. Battery management systems

1.7. Summary


Chapter 2 Battery Modelling

2.1. Background

2.2. Classification

2.3. Black box model

2.4. Equivalent circuit models

2.4.1. General n-RC model

2.4.2. Models with different numbers of RC networks

2.4.3. Open circuit voltage

2.4.4. Polarisation characteristics

2.5. Experiments

2.6. Parameter identification methods

2.6.1. Offline parameters identification method (OFFPIM)

2.6.2. Online parameters identification method (ONPIM)

2.7. Case study

2.7.1. Testing data

2.7.2. Case one-OFFPIM

2.7.3. Case two-ONPIM

2.7.4. Discussions

2.8. Model uncertainties

2.8.1. Battery aging

2.8.2. Battery type

2.8.3. Battery temperature

2.9. Other battery models

2.10. Summary


Chapter 3 Battery State of Charge (SOC) and State of Energy (SOE) Estimation

3.1 Background

3.2. Classification  

3.2.1. Looking-up table based method

3.2.2. Ampere-hour integral method

3.2.3. Data-driven estimation methods

3.2.4. Model-based estimation methods

3.3. Model-based SOC estimation methods

3.3.1. Discrete-time realisation algorithm

3.3.2. Extended Kalman filter

3.3.3. H infinity filter

3.3.4. Case study

3.3.5. Influences of uncertainties on SOC estimation

3.4. Model-based SOC estimation method

3.4.1 Real-time modeling process

3.4.2. Case study

3.5. Model-based SOE estimation method with identified model parameters in real-time

3.5.1. SOE definition

3.5.2. State space equation

3.5.3. Case study

3.5.4. Influences of uncertainties on SOE estimation

3.6. Summary


Chapter 4 Battery State of Health Estimation

4.1. Background

4.2. Experimental methods

4.2.1. Direct measurement methods

4.2.3. Indirect analysis methods

4.3. Model-based methods

4.3.1. Adaptive state estimation methods

4.3.2. Data-driven methods

4.4. Joint estimation method

4.4.1. Relationship between SOC and capacity

4.4.2. Case study

4.5. Dual Estimation Method

4.5.1. Implementation with adaptive extended Kalman Filter algorithm

4.5.2. SOC-SOH estimation

4.5.3. Case study

4.6. Summary


Chapter 5 State of Power Estimation

5.1. Background

5.2. Instantaneous state of power (SOP) estimation methods  

5.2.1. HPPC method  

5.2.2. The SOC-limited method

5.2.3. Voltage-limited method

5.2.4. Multi-constraints dynamic (MCD) method

5.2.5. Case study

5.3. Continuous SOP estimation method

5.3.1. Continuous peak current estimation

5.3.2. Continuous SOP estimation

5.3.3. Influences of battery states and parameters on SOP estimation

5.4. Summary


Chapter 6 Battery Charging

6.1. Background

6.2. Basic terms for evaluating charging performances

6.3. Battery charging methods

6.3.1. Constant current and constant voltage charging

6.3.2. Multistep constant current charging

6.3.3. Two-step constant current constant voltage charging

6.3.4. Constant voltage constant current constant voltage charging

6.3.5. Pulse charging

6.3.6. Charging termination

6.3.7. Comparison of charging algorithms for Lithium ion batteries

6.4. Optimal charging current profiles for lithium ion batteries

6.5. Lithium titanate oxide battery with extreme fast charging capability

6.6. Summary


Chapter 7 Battery Balancing

7.1. Background

7.2. Battery sorting

7.2.1. Battery sorting based on capacity and internal resistance

7.2.2. Battery sorting based on self-organising map

7.3. Battery passive balancing

7.3.1. Fixed shunt resistor

7.3.2. Switched shunt resistor

7.3.3. Shunt transistor

7.4. Battery active balancing

7.4.1. Balancing criterion

7.4.2. Balancing control

7.4.3. Balancing circuits

7.5. Battery active balancing systems

7.5.1 Active balancing system based on SOC as a balancing criterion

7.5.2 Active balancing system based on fuzzy logic controller

7.6 Summary


Chapter 8 Battery Energy Management Systems in Electric Vehicles

8.1. Background

8.2. Battery management systems (BMSs)

8.2.1. Typical structure of BMSs

8.2.2. Representative products

8.3. Key points of BMSs in future generation

8.3.1. Self-heating

8.3.2. Safety management

8.3.3. Cloud computing

8.4. Summary