Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : : Manufacturing, Operation and Reutilization.
Saved in:
Superior document: | Green Energy and Technology Series |
---|---|
: | |
TeilnehmendeR: | |
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
|
Online Access: | |
Physical Description: | 1 online resource (277 pages) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
5006950332 |
---|---|
ctrlnum |
(MiAaPQ)5006950332 (Au-PeEL)EBL6950332 (OCoLC)1310785741 |
collection |
bib_alma |
record_format |
marc |
spelling |
Liu, Kailong. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. 1st ed. Cham : Springer International Publishing AG, 2022. ©2022. 1 online resource (277 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Green Energy and Technology Series 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Wang, Yujie. Lai, Xin. Print version: Liu, Kailong Data Science-Based Full-Lifespan Management of Lithium-Ion Battery Cham : Springer International Publishing AG,c2022 9783031013393 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6950332 Click to View |
language |
English |
format |
eBook |
author |
Liu, Kailong. |
spellingShingle |
Liu, Kailong. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. Green Energy and Technology Series 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. |
author_facet |
Liu, Kailong. Wang, Yujie. Lai, Xin. |
author_variant |
k l kl |
author2 |
Wang, Yujie. Lai, Xin. |
author2_variant |
y w yw x l xl |
author2_role |
TeilnehmendeR TeilnehmendeR |
author_sort |
Liu, Kailong. |
title |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. |
title_sub |
Manufacturing, Operation and Reutilization. |
title_full |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. |
title_fullStr |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. |
title_full_unstemmed |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. |
title_auth |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. |
title_new |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : |
title_sort |
data science-based full-lifespan management of lithium-ion battery : manufacturing, operation and reutilization. |
series |
Green Energy and Technology Series |
series2 |
Green Energy and Technology Series |
publisher |
Springer International Publishing AG, |
publishDate |
2022 |
physical |
1 online resource (277 pages) |
edition |
1st ed. |
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. |
isbn |
9783031013409 9783031013393 |
callnumber-first |
T - Technology |
callnumber-subject |
TA - General and Civil Engineering |
callnumber-label |
TA401-492 |
callnumber-sort |
TA 3401 3492 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6950332 |
illustrated |
Not Illustrated |
oclc_num |
1310785741 |
work_keys_str_mv |
AT liukailong datasciencebasedfulllifespanmanagementoflithiumionbatterymanufacturingoperationandreutilization AT wangyujie datasciencebasedfulllifespanmanagementoflithiumionbatterymanufacturingoperationandreutilization AT laixin datasciencebasedfulllifespanmanagementoflithiumionbatterymanufacturingoperationandreutilization |
status_str |
n |
ids_txt_mv |
(MiAaPQ)5006950332 (Au-PeEL)EBL6950332 (OCoLC)1310785741 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Green Energy and Technology Series |
is_hierarchy_title |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization. |
container_title |
Green Energy and Technology Series |
author2_original_writing_str_mv |
noLinkedField noLinkedField |
marc_error |
Info : MARC8 translation shorter than ISO-8859-1, choosing MARC8. --- [ 856 : z ] |
_version_ |
1792331062427254784 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06135nam a22004453i 4500</leader><controlfield tag="001">5006950332</controlfield><controlfield tag="003">MiAaPQ</controlfield><controlfield tag="005">20240229073845.0</controlfield><controlfield tag="006">m o d | </controlfield><controlfield tag="007">cr cnu||||||||</controlfield><controlfield tag="008">240229s2022 xx o ||||0 eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783031013409</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9783031013393</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(MiAaPQ)5006950332</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(Au-PeEL)EBL6950332</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1310785741</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">MiAaPQ</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">MiAaPQ</subfield><subfield code="d">MiAaPQ</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">TA401-492</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Kailong.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Science-Based Full-Lifespan Management of Lithium-Ion Battery :</subfield><subfield code="b">Manufacturing, Operation and Reutilization.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham :</subfield><subfield code="b">Springer International Publishing AG,</subfield><subfield code="c">2022.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2022.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (277 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Green Energy and Technology Series</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yujie.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lai, Xin.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Liu, Kailong</subfield><subfield code="t">Data Science-Based Full-Lifespan Management of Lithium-Ion Battery</subfield><subfield code="d">Cham : Springer International Publishing AG,c2022</subfield><subfield code="z">9783031013393</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Green Energy and Technology Series</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6950332</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |