Data Science-Based Full-Lifespan Management of Lithium-Ion Battery: Manufacturing, Operation and Reutilization (Green Energy and Technology)

Foreword by Prof. Qing-Long Han
Foreword by Prof. Jinyue Yan
Preface
Acknowledgments
Contents
About the Authors
Abbreviations
1 Introduction to Battery Full-Lifespan Management
	1.1 Background and Motivation
		1.1.1 Energy Storage Market
		1.1.2 Li-Ion Battery Role
	1.2 Li-Ion Battery and Its Management
		1.2.1 Li-Ion Battery
		1.2.2 Demands for Battery Management
	1.3 Data Science Technologies
		1.3.1 What is Data Science
		1.3.2 Type of Data Science Technologies
		1.3.3 Performance Indicators
	1.4 Summary
	References
2 Key Stages for Battery Full-Lifespan Management
	2.1 Full-Lifespan of Li-Ion Battery
	2.2 Li-Ion Battery Manufacturing
		2.2.1 Battery Manufacturing Fundamental
		2.2.2 Identifying Manufacturing Parameters and Variables
	2.3 Li-Ion Battery Operation
		2.3.1 Battery Operation Fundamental
		2.3.2 Key Tasks of Battery Operation Management
	2.4 Li-Ion Battery Reutilization
	2.5 Summary
	References
3 Data Science-Based Battery Manufacturing Management
	3.1 Overview of Battery Manufacturing
	3.2 Data Science Application of Battery Manufacturing Management
		3.2.1 Data Science Framework for Battery Manufacturing Management
		3.2.2 Machine Learning Tool
	3.3 Battery Electrode Manufacturing
		3.3.1 Overview of Battery Electrode Manufacturing
		3.3.2 Case 1: Battery Electrode Mass Loading Prediction with GPR
		3.3.3 Case 2: Battery Electrode Property Classification with RF
	3.4 Battery Cell Manufacturing
		3.4.1 Overview of Battery Cell Manufacturing
		3.4.2 Case 1: Battery Cell Capacities Prediction with SVR
		3.4.3 Case 2: Battery Cell Capacity Classification with RUBoost
	3.5 Summary
	References
4 Data Science-Based Battery Operation Management I
	4.1 Battery Operation Modelling
		4.1.1 Battery Electrical Model
		4.1.2 Battery Thermal Model
		4.1.3 Battery Coupled Model
	4.2 Battery State Estimation
		4.2.1 Battery SoC Estimation
		4.2.2 Battery SoP Estimation
		4.2.3 Battery SoH Estimation
		4.2.4 Joint State Estimation
	4.3 Summary
	References
5 Data Science-Based Battery Operation Management II
	5.1 Battery Ageing Prognostics
		5.1.1 Ageing Mechanism and Stress Factors
		5.1.2 Li-Ion Battery Lifetime Prediction with Data Science
		5.1.3 Case 1: Li-Ion Battery Cyclic Ageing Predictions with Modified GPR
		5.1.4 Case 2: Li-Ion Battery Lifetime Prediction with LSTM and GPR
	5.2 Battery Fault Diagnosis
		5.2.1 Overview of Data Science-Based Battery Fault Diagnosis Methods
		5.2.2 Case: ISC Fault Detection Based on SoC Correlation
	5.3 Battery Charging
		5.3.1 Battery Charging Objective
		5.3.2 Case 1: Li-Ion Battery Economic-Conscious Charging
		5.3.3 Case 2: Li-Ion Battery Pack Charging with Distributed Average Tracking
	5.4 Summary
	References
6 Data Science-Based Battery Reutilization Management
	6.1 Overview of Battery Echelon Utilization and Material Recycling
		6.1.1 Echelon Utilization
		6.1.2 Material Recycling
	6.2 Sorting of Retired Li-Ion Batteries Based on Neural Network
		6.2.1 Data Science-Based Sorting Criteria
		6.2.2 Case 1: Sorting Criteria Estimation Based on Charging Data
		6.2.3 Case 2: Sorting Criteria Estimation Based on EIS
	6.3 Regrouping Methods of Retired Li-Ion Batteries
		6.3.1 Overview of Regrouping Methods
		6.3.2 Case 1: Hard Clustering of Retired Li-Ion Batteries Using K-means
		6.3.3 Case 2: Soft Clustering of Retired Li-Ion Batteries Based on EIS
	6.4 Material Recycling Method of Spent Li-Ion Batteries
		6.4.1 Main Recycling Methods
		6.4.2 Case 1: Physical Recycling Technologies
		6.4.3 Case 2: Chemical Recycling Technologies
	6.5 Summary
	References
7 The Ways Ahead
	7.1 Data Science-Based Battery Manufacturing
		7.1.1 Continuous Manufacturing Line
		7.1.2 Digital Manufacturing Line
		7.1.3 Advanced Sensing Methodology
		7.1.4 Improved Machine Learning
	7.2 Data Science-Based Battery Operation
		7.2.1 Operation Modelling and State Estimation
		7.2.2 Lifetime Prognostics
		7.2.3 Fault Diagnostics
		7.2.4 Battery Charging
	7.3 Data Science-Based Battery Reutilization
	7.4 Summary
	References