Robust Battery Management System Design With MATLAB

✍ Scribed by Balakumar Balasingam

• Publisher: Artech House
• Year: 2023
• Tongue: English
• Leaves: 305
• Series: Artech House Power Engineering Library
• Category: Library

✦ Synopsis

This book provides model-based solutions to various battery management problems, including battery impedance estimation, battery capacity estimation, state of charge estimation, state of health estimation, battery thermal management, and optimal charging algorithms. The book introduces important battery management problems in a modularized fashion, decoupling each battery management problem from others as much as possible, allowing you to focus on understanding a particular topic rather than having to understand all aspects of a battery management system. You will get the necessary background to understand, implement and improve battery fuel gauges in electric vehicles, and general state of health of the battery; use proven models and algorithms to estimate the thermal properties of a battery; and know the basics of smart battery charger design. You will also be equipped to accurately estimate battery features of vehicles, such as state of charge, expected charging time, and state of health, to make customized charging waveforms for each vehicle. The book teaches you how to create simulation environments to test and validate algorithms against model uncertainty and measurement noise. In addition, the importance of benchmarking battery management algorithms is covered, and several bench marking metrics are presented. Included MATLAB codes give you an easy way to test the algorithms using realistic data and to develop and test alternative solutions. This is a useful and timely guide for battery engineers at all levels, as well as research scientists and advanced students working in this robust and rapidly advancing area.

✦ Table of Contents

Robust Battery ManagementSystem Design with MATLAB®
Contents
Preface
Chapter 1
About This Book
1.1
Introduction
1.2
Who Is This Book For?
1.3
Use Cases
1.3.1
Remaining Mileage Estimation in an Electric Vehicle
1.3.2
Generating Battery Replacement Warning
1.3.3
Estimating the Expected Temperature Rise in a Battery Pack
1.3.4
Smart Battery Charger Design
1.3.5
EV Fleet Management
1.3.6
Teaching a Graduate-Level Course on BMS
1.4
What Is Novel in This Book?
1.4.1
Modularized Approach
1.4.2
Illustration of Algorithms Through Matlab Simulation
1.4.3
Emphasis on Both Theoretical and Practical Aspects
1.5
Organization of This Book
1.6
Matlab Codes
1.7
Bibliographical Notes
References
Chapter 2
Review of Required Mathematics
2.1
Introduction
2.2
Least Squares Estimator
2.3
Kalman Filter
2.4
Extended Kalman Filter
2.4.1
Assumptions of the EKF
2.5
Conclusions
2.6
Bibliographical Notes
2.7
Problems
References
Chapter 3
Battery Modeling
3.1
Introduction
3.2
Elements of Electrical Equivalent Circuit Models
3.2.1
DC Equivalent Circuit Model
3.2.2
AC Equivalent Circuit Model
3.3
Reduced-Order Models
3.3.1
Ideal Battery Model
3.3.2
Open-Circuit Voltage Model
3.3.3
Relaxation Model
3.3.4
Hysteresis Model
3.3.5
Enhanced Self-Correcting Model
3.3.6
R-int Model
3.3.7
Other Reduced-Order Models
3.4
Battery Power
3.5
Battery Capacity
3.5.1
Total Capacity
3.5.2
Discharge Capacity
3.5.3
Rated Capacity
3.5.4
Custom-Defined Capacity
3.6
State of Health
3.7
Battery Packs
3.8
Battery Simulator
3.9
Summary
3.10
Bibliographical Notes
References
Chapter 4
Open-Circuit Voltage Characterization
4.1
Introduction
4.2
Empirical OCV-SOC Models
4.2.1
Linear Regression Models
4.2.2
Nonlinear Regression Models
4.2.3
Hybrid or Piecewise Linear Models
4.2.4
Tabular Model
4.3
OCV-SOC Model Parameter Estimation
4.3.1
Linear Least-Squares
4.3.2
Nonlinear Least-Squares
4.3.3
Hybrid Estimation
4.3.4
Tabular Model Estimation
4.4
Model Selection Metrics
4.4.1
OCV Prediction Error
4.4.2
Model Evaluation Metrics
4.4.3
Computational Complexity
4.4.4
Numerical Stability
4.4.5
System Requirement
4.5
Selection of the OCV-SOC Model
4.6
Summary
4.7
Bibliographical Notes
References
Chapter 5
Frequency-Domain Approaches to Battery ECM Identification
5.1
Introduction
5.2
Frequency Response of a Battery
5.3
Computing Frequency Response Using DFT
5.4
ECM Parameter Estimation Problem
5.5
Approximate Estimation of ECM Parameters
5.6
Causes of Parameter Estimation Error
5.6.1
Effect of Approximation
5.6.2
Effect of Measurement Noise
5.7
Improved Approach for Parameter Estimation
5.7.1
Estimation of the Warburg Coefficient
5.7.2
Estimation of the CT Components
5.7.3
Estimation of the SEI Components
5.7.4
Estimation of Resistance and Inductance
5.7.5
Feature Point Extraction
5.8
Demonstration
5.8.1
Demonstration Using Simulated Data
5.8.2 Demonstration Using Real Data
5.9
Summary
5.10
Bibliographical Notes
References
Chapter 6
Time-Domain Approaches to Battery ECM Identification
6.1
Introduction
6.2
Signal Model of a Battery
6.3
ECM Identification of Different Model Orders
6.4
Parameter Estimation Method
6.5
Performance Analysis
6.6
Simulation Analysis
6.6.1
Perfect ECM Assumption
6.6.2
Realistic ECM Assumption
6.6.3
Real Data
6.7
Summary
6.8
Bibliographical Notes
References
Chapter 7
Battery Capacity Estimation
7.1
Introduction
7.2
Basics of Battery Capacity Estimation
7.2.1
Offline Estimation of Battery Capacity
7.2.2
Real-Time Capacity Estimation
7.3
Capacity Estimation in the Presence of Noise
7.3.1
LS Estimate
7.3.2
TLS Estimate
7.4
Recursive Estimates
7.4.1
Recursive LS
7.4.2
Recursive TLS
7.4.3
KF-Based Fusion
7.5
Experimental Results
7.5.1
OCV-SOC Characterization Test
7.5.2
Dynamic Discharge-Charge Profile
7.5.3
Real-Time Capacity Estimation
7.6
Conclusions
7.7
Bibliographical Notes
References
Chapter 8
Battery Fuel Gauging
8.1
Introduction
8.1.1
State of Charge
8.1.2
Time to Shut Down
8.1.3
State of Health
8.1.4
Remaining Useful Life
8.2
SOC Estimation: Coulomb Counting Approach
8.3
SOC Estimation: An OCV-Based Approach
8.4
SOC Estimation: Fusion Approach
8.4.1
Measurement Model
8.4.2
Scaling
8.4.3
Extended Kalman Filter for SOC Tracking
8.5
Filter Consistency Testing Approaches
8.5.1
Normalized Innovation Squared
8.5.2
Zero-Mean Test of Innovations
8.6
Results
8.7
Conclusions
8.8
Bibliographical Notes
References
Chapter 9
Battery Thermal Management
9.1
Introduction
9.2
Thermal Management Mediums
9.2.1
Air
9.2.2
Liquid
9.2.3
Phase Change Material
9.3
Battery Thermal Modeling
9.4
Simulation Results
9.5
Conclusions
9.6
Bibliographical Notes
References
Chapter 10
Optimal Charging Algorithms
10.1
Introduction
10.2
Charging Strategies
10.2.1
Constant Current Charging
10.2.2
Constant Voltage Charging
10.2.3
Constant Current-Constant Voltage Charging
10.2.4
Multistage Constant Current Charging
10.2.5
Pulse Charging
10.2.6
Trickle Charging
10.2.7
Float Charging
10.3
Optimized Charging Strategies
10.4
Numerical Results
10.5 Summary
10.6
Bibliographical Notes
References
Chapter 11
Evaluation and Benchmarking of Battery Management Systems
11.1
Introduction
11.2
Coulomb Counting Metric
11.3
OCV-SOC Metric
11.4
TTV Metric
11.5
Demonstration of the BFG Evaluation
11.6
Summary
11.7
Bibliographical Notes
References
Appendix A:
Closed-Form Derivation of the TLS Estimate
Appendix B: Formal Derivation of Capacity
B.1
Transformation of the Inverse Estimates
B.2
The Expected Value of y
B.3
The Variance of the Expected Value of y
References
Appendix C:
Discretization of the State-Space Model
List of Acronyms
About the Author
Index