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

By: Liu, Kailong.
Contributor(s): Wang, Yujie | Lai, Xin.
Material type: materialTypeLabelBookSeries: Green Energy and Technology Series: Publisher: Cham : Springer International Publishing AG, 2022Copyright date: �2022Edition: 1st ed.Description: 1 online resource (277 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783031013409.Genre/Form: Electronic books.Online resources: Click to View
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.
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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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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