Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization
Liu, Kailong
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization by Kailong Liu, Yujie Wang, Xin Lai - 1st edition 2022 - 1 recurso en línea (XXIII, 258 páginas) 163 ilustraciones, 158 ilustraciones a color - Green Energy and Technology 1865-3537 .
Chapter 1. Introduction to Battery Full-Lifespan Management -- Chapter 2. Key Stages for Battery Full-Lifespan Management -- Chapter 3. Data Science-based Battery Manufacturing Management -- Chapter 4. Data Science-based Battery Operation Management I -- Chapter 5. Data Science-based Battery Operation Management II -- Chapter 6. Data Science-based Battery Reutilization Management -- Chapter 7. The Ways Ahead.
Open Access
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.
9783031013409
10.1007/978-3-031-01340-9 doi
Almacenamiento de energía
TK2945.L58 / 2022 EB
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization by Kailong Liu, Yujie Wang, Xin Lai - 1st edition 2022 - 1 recurso en línea (XXIII, 258 páginas) 163 ilustraciones, 158 ilustraciones a color - Green Energy and Technology 1865-3537 .
Chapter 1. Introduction to Battery Full-Lifespan Management -- Chapter 2. Key Stages for Battery Full-Lifespan Management -- Chapter 3. Data Science-based Battery Manufacturing Management -- Chapter 4. Data Science-based Battery Operation Management I -- Chapter 5. Data Science-based Battery Operation Management II -- Chapter 6. Data Science-based Battery Reutilization Management -- Chapter 7. The Ways Ahead.
Open Access
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.
9783031013409
10.1007/978-3-031-01340-9 doi
Almacenamiento de energía
TK2945.L58 / 2022 EB