Multidimensional Lithium-Ion Battery Status Monitoring focuses on equivalent circuit modeling, parameter identification, and state estimation in lithium-ion battery power applications. It explores the requirements of high-power lithium-ion batteries for new energy vehicles and systematically describes the key technologies in core state estimation based on battery equivalent modeling and parameter identification methods of lithium-ion batteries, providing a technical reference for the design and application of power lithium-ion battery management systems.
- Reviews Li-ion battery characteristics and applications.
- Covers battery equivalent modeling, including electrical circuit modeling and parameter identification theory
- Discusses battery state estimation methods, including state of charge estimation, state of energy prediction, state of power evaluation, state of health estimation, and cycle life estimation
- Introduces equivalent modeling and state estimation algorithms that can be applied to new energy measurement and control in large-scale energy storage
- Includes a large number of examples and case studies
This book has been developed as a reference for researchers and advanced students in energy and electrical engineering.
Author(s): Shunli Wang, Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez, Josep M Guerrero
Series: Emerging Materials and Technologies
Publisher: CRC Press
Year: 2022
Language: English
Pages: 354
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Battery Characteristics and Parameters
1.1 Overview of Lithium-Ion Batteries
1.1.1 State of the Art
1.1.2 Lithium-Ion Battery Composition
1.1.3 Battery Working Principle
1.1.4 Development Prospects
1.2 The Types and Characteristics of Lithium-Ion Batteries
1.2.1 Lithium Iron Phosphate Battery
1.2.2 Lithium Cobaltate Battery
1.2.3 Lithium-Manganese Battery
1.3 Basic Parameters
1.3.1 Voltage
1.3.2 Capacity
1.3.3 Internal Resistance
1.3.4 Polarization Characteristics
1.3.5 Energy and Power Density
1.4 Core Battery State Factors
1.4.1 State of Charge
1.4.2 State of Health
1.4.3 State of Power
1.4.4 State of Energy
1.4.5 Remaining Useful Life
1.4.6 Temperature Performance
1.5 Conclusion
Chapter 2 Equivalent Modeling and Parameter Identification
2.1 Overview of Battery Equivalent Circuit Modeling
2.2 Modeling Types and Concepts
2.3 Equivalent Circuit Modeling
2.3.1 Rint Model
2.3.2 RC Model
2.3.3 Thevenin Model
2.3.4 PNGV Model
2.3.5 GNL Equivalent Model
2.3.6 Second-Order Equivalent Model
2.3.7 Compound Equivalent Model
2.3.8 Voltage Matching Equivalent Circuit Model
2.3.9 Improved Second-Order RC Equivalent Model
2.4 Introduction of Common Working Conditions
2.4.1 Hybrid Pulse Power Characterization Test
2.4.2 Beijing Bus Dynamic Stress Test
2.4.3 Dynamic Stress Test
2.5 Offline Parameter Identification
2.5.1 Point Calculation
2.5.2 Curve Fitting
2.5.3 Equivalent Circuit Model Parameter Validation
2.5.4 Model Parameter Identification
2.5.5 Double-Exponential Fitting Results
2.5.6 Experimental Verification
2.6 Online Parameter Identification
2.6.1 Recursive Least-Square Method
2.6.2 Bias Compensation Method
2.6.3 Forgetting Factor—RLS Method
2.6.4 FFRLS-Based Second-Order RC Model Parameter Identification
2.6.5 Multi-Innovation Least-Square Method
2.6.6 Extended Kalman Filter and Verification
2.6.7 Comparison of Thevenin and Second-Order RC Modeling
2.7 Conclusion
Chapter 3 Battery State of Charge Estimation
3.1 Overview of SOC Estimation
3.1.1 Definition of SOC
3.1.2 Main Affecting Factor Analysis
3.1.3 Traditional Estimation Methods
3.2 Kalman Filter and Modifications
3.2.1 Kalman Filter
3.2.2 Extended Kalman Filter
3.2.3 Improved Extended Kalman Filter
3.2.4 Unscented Kalman Filter
3.2.5 Adaptive Kalman Filter
3.2.6 Cubature Kalman Filter
3.3 SOC Estimation Based on the Second-Order RC Model
3.3.1 Second-Order RC Modeling
3.3.2 Parameter Identification
3.3.3 EKF-Based Calculation Procedure
3.4 EKF-Based SOC Estimation
3.4.1 Parameter Identification
3.4.2 Iterative Calculation Algorithm
3.4.3 Experimental Analysis
3.4.4 Thevenin Model-Based Estimation
3.4.5 Improved PNGV-Based Estimation
3.5 UKF-Based SOC Estimation
3.5.1 Equivalent Circuit Modeling
3.5.2 Unscented Transformation
3.5.3 UKF Algorithm Analysis
3.5.4 Unscented Kalman Filtering Steps
3.5.5 Experimental Verification
3.6 AEKF-Based SOC Estimation
3.6.1 Equivalent Circuit Modeling
3.6.2 AEKF Algorithm Analysis
3.6.3 Experimental Analysis
3.7 SOC Estimation Based on Other Algorithms
3.7.1 Particle Filtering
3.7.2 Extended Particle Filtering
3.7.3 Unscented Particle Filter
3.7.4 Backpropagation Neural Network
3.7.5 Dual-Extended Kalman Filter
3.7.6 Strong Tracking-Adaptive Extended Kalman Filter
3.7.7 Cubature Kalman Filter
3.7.8 Square Root Cubature Kalman Filter
3.7.9 Adaptive H∞ Filter
3.7.10 NARX-EKF Network
3.8 SOC Estimation Based on GA-BP Algorithm
3.8.1 Backpropagation Neural Network
3.8.2 Genetic Algorithm
3.8.3 Experimental Analysis
3.9 Online Estimation Algorithm
3.9.1 Improved PNGV Equivalent Circuit Modeling
3.9.2 Online Identification of Forgetting Factor RLS Algorithm
3.9.3 Online Parameter Identification Effect
3.9.4 Online Identification and Estimation Results
3.10 Conclusion
Chapter 4 Battery State of Health Estimation
4.1 Overview of Battery Health Indicator
4.2 Model-Based SOH Estimation
4.2.1 Thevenin Equivalent Modeling
4.2.2 Kalman Filter and Its Extension
4.2.3 Extended Particle Filter
4.2.4 Dual Adaptive Kalman Filter
4.3 Data-Driven SOH Estimation
4.3.1 Support Vector Machine
4.3.2 Deep Convolutional Network
4.3.3 Machine Learning
4.3.4 Fusion Modeling
4.3.5 IC-Based SOH Estimation
4.4 Conclusion
Chapter 5 Battery State of Power Estimation
5.1 Overview of SOP Estimation
5.2 Interpolation-Based SOP Estimation
5.3 Model-Based SOP Estimation
5.3.1 Linear Modeling
5.3.2 RC Loop Modeling
5.3.3 SOC Limit Estimation
5.3.4 Temperature Limit Estimation
5.4 Data-Driven Non-Parametric Modeling
5.4.1 BP Neural Network
5.4.2 Adaptive Neuro-Fuzzy Inference Modeling
5.4.3 Support Vector Machine
5.5 Other Estimation Methods
5.6 Conclusion
Chapter 6 Battery State of Energy and Cycle Life Estimation
6.1 Overview of SOE Estimation
6.1.1 SOE Definition
6.1.2 Traditional SOE Estimation Method
6.1.3 SOE Integral Calculation Expression
6.2 UKF-Based SOE Estimation
6.2.1 SOE Estimation Model Structure Establishment
6.2.2 SOE Estimation Procedure Design
6.2.3 AUKF-Based SOE Estimation
6.2.4 Initial State Determination
6.2.5 DEKF-Based SOE Estimation
6.2.6 Comparative SOE Estimation Results
6.3 Overview of Battery Cycle Life
6.3.1 Definition of Basic Concepts
6.3.2 Factors Affecting Battery Life
6.4 Experiment-Based Battery Life Estimation
6.4.1 Capacity Method
6.4.2 Cycle Number Method
6.4.3 Weighted Ampere-Hour Method
6.4.4 Internal Resistance Method
6.4.5 Adaptive Filtering Method
6.4.6 Multidimensional Extended Kalman Filter
6.4.7 Multidimensional Particle Filter
6.5 Data-Driven Forecasting
6.5.1 Machine Learning
6.5.2 Support Vector Regression
6.5.3 RUL Prediction Based on Shallow Learning
6.5.4 Artificial Neural Networks
6.5.5 The Autoregressive Integrated Moving Average Model
6.6 Conclusion
References
Index
