Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : : Manufacturing, Operation and Reutilization.

• Superior document: Green Energy and Technology Series
• Author: Liu, Kailong.
• TeilnehmendeR: Wang, Yujie.; Lai, Xin.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2022.; ©2022.
• Year of Publication: 2022
• Edition: 1st ed.
• Language: English
• Series: Green Energy and Technology Series
• Physical Description: 1 online resource (277 pages)

Table of Contents

• Intro
• 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.