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

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) batter...

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Main Authors: Liu, Kailong, Wang, Yujie, Lai, Xin
Format: eBook
Language:English
Published: Cham Springer Nature 2022
Springer International Publishing AG
Edition:1
Series:Green Energy and Technology
Subjects:
Artificial Intelligence
Battery Manufacturing Management
Battery Operation Management
Battery Recycling Management
Catalysis
Computer Technology
Computing and Information Technology
Data Science
Electric batteries
Electric power production
Energy technology and engineering
Engineering
Engineering—Data processing
Force and energy
Lithium-ion Battery
Materials
Materials science
Mechanical engineering and materials
Nonfiction
Open Access
Technology
Technology, Engineering, Agriculture, Industrial processes
ISBN:3031013409, 9783031013409, 9783031013393, 3031013395
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Abstract This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.
AbstractList This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.
This open access book comprehensively consolidates studies in the rapidly emerging field of battery management.The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery.
Author Liu, Kailong
Wang, Yujie
Lai, Xin
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Snippet This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and...
This open access book comprehensively consolidates studies in the rapidly emerging field of battery management.The primary focus is to overview the new and...
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SubjectTerms Artificial Intelligence
Battery Manufacturing Management
Battery Operation Management
Battery Recycling Management
Catalysis
Computer Technology
Computing and Information Technology
Data Science
Electric batteries
Electric power production
Energy technology and engineering
Engineering
Engineering—Data processing
Force and energy
Lithium-ion Battery
Materials
Materials science
Mechanical engineering and materials
Nonfiction
Open Access
Technology
Technology, Engineering, Agriculture, Industrial processes
SubjectTermsDisplay Computer Technology.
Electronic books.
Engineering.
Nonfiction.
Technology.
Subtitle Manufacturing, Operation and Reutilization
TableOfContents 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
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
Title Data Science-Based Full-Lifespan Management of Lithium-Ion Battery
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