Robust Battery Management System Design With MATLAB

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