| 000 -LEADER |
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| fixed length control field |
06199nam a22004693i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
EBC6950332 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240322152826.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
231028s2022 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031013409 |
| Qualifying information |
(electronic bk.) |
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| Cancelled/invalid ISBN |
9783031013393 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(MiAaPQ)EBC6950332 |
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| System control number |
(Au-PeEL)EBL6950332 |
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|---|
| System control number |
(OCoLC)1310785741 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
MiAaPQ |
| Language of cataloging |
eng |
| Description conventions |
rda |
| -- |
pn |
| Transcribing agency |
MiAaPQ |
| Modifying agency |
MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA401-492 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Liu, Kailong. |
| 245 10 - TITLE STATEMENT |
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| Title |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : |
| Remainder of title |
Manufacturing, Operation and Reutilization. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
1st ed. |
| 264 #1 - |
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| -- |
Cham : |
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Springer International Publishing AG, |
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2022. |
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| -- |
�2022. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (277 pages) |
| 336 ## - |
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text |
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txt |
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rdacontent |
| 337 ## - |
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computer |
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c |
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rdamedia |
| 338 ## - |
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online resource |
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cr |
| -- |
rdacarrier |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Green Energy and Technology Series |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
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. |
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| Formatted contents note |
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. |
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| Formatted contents note |
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. |
| 588 ## - |
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| -- |
Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) |
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| Local note |
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 655 #4 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Wang, Yujie. |
|
|---|
| Personal name |
Lai, Xin. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Print version: |
| Main entry heading |
Liu, Kailong |
| Title |
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery |
| Place, publisher, and date of publication |
Cham : Springer International Publishing AG,c2022 |
| International Standard Book Number |
9783031013393 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) |
|---|
| Corporate name or jurisdiction name as entry element |
ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Green Energy and Technology Series |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://ebookcentral.proquest.com/lib/kliuc-ebooks/detail.action?docID=6950332 |
| Public note |
Click to View |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
E-book |
