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New paradigms in flow battery modelling

Shah, Akeel A.

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New paradigms in flow battery modelling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New paradigms in flow battery modelling/ by Akeel A. Shah ... [et al.].
其他作者:	Shah, Akeel A.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	x, 381 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction to Energy Storage -- Chapter 2: Introduction to Flow Batteries -- Chapter 3: An Introduction Flow Battery Modelling -- Chapter 4: Latest Developments in Macroscale Models -- Chapter 5: Latest Developments in Ab-Initio to Mesoscopic Models -- Chapter 6: Machine Learning for Flow Battery Systems -- Chapter 7: Future Flow Battery Modelling -- Bibliography.
Contained By:	Springer Nature eBook
標題:	Flow batteries - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-981-99-2524-7
ISBN:	9789819925247

New paradigms in flow battery modelling
New paradigms in flow battery modelling[electronic resource] /by Akeel A. Shah ... [et al.]. - Singapore :Springer Nature Singapore :2023. - x, 381 p. :ill., digital ;24 cm. - Engineering applications of computational methods,v. 162662-3374 ;. - Engineering applications of computational methods ;v. 16..

Chapter 1: Introduction to Energy Storage -- Chapter 2: Introduction to Flow Batteries -- Chapter 3: An Introduction Flow Battery Modelling -- Chapter 4: Latest Developments in Macroscale Models -- Chapter 5: Latest Developments in Ab-Initio to Mesoscopic Models -- Chapter 6: Machine Learning for Flow Battery Systems -- Chapter 7: Future Flow Battery Modelling -- Bibliography.

This book provides a comprehensive review of the latest modelling developments in flow batteries, as well as some new results and insights. Flow batteries have long been considered the most flexible answer to grid scale energy storage, and modelling is a key component in their development. Recent modelling has moved beyond macroscopic methods, towards mesoscopic and smaller scales to select materials and design components. This is important for both fundamental understanding and the design of new electrode, catalyst and electrolyte materials. There has also been a recent explosion in interest in machine learning for electrochemical energy technologies. The scope of the book includes these latest developments and is focused on advanced techniques, rather than traditional modelling paradigms. The aim of this book is to introduce these concepts and methods to flow battery researcher, but the book would have a much broader appeal since these methods also employed in other battery and fuel cell systems and far beyond. The methods will be described in detail (necessary fundamental material in Appendices) The book appeals to graduate students and researchers in academia/industry working in electrochemical systems, or those working in computational chemistry/machine learning wishing to seek new application areas.

ISBN: 9789819925247

Standard No.: 10.1007/978-981-99-2524-7doiSubjects--Topical Terms:

3664981
Flow batteries
--Mathematical models.

LC Class. No.: TK2945.F56

Dewey Class. No.: 621.312424015118

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520 $a This book provides a comprehensive review of the latest modelling developments in flow batteries, as well as some new results and insights. Flow batteries have long been considered the most flexible answer to grid scale energy storage, and modelling is a key component in their development. Recent modelling has moved beyond macroscopic methods, towards mesoscopic and smaller scales to select materials and design components. This is important for both fundamental understanding and the design of new electrode, catalyst and electrolyte materials. There has also been a recent explosion in interest in machine learning for electrochemical energy technologies. The scope of the book includes these latest developments and is focused on advanced techniques, rather than traditional modelling paradigms. The aim of this book is to introduce these concepts and methods to flow battery researcher, but the book would have a much broader appeal since these methods also employed in other battery and fuel cell systems and far beyond. The methods will be described in detail (necessary fundamental material in Appendices) The book appeals to graduate students and researchers in academia/industry working in electrochemical systems, or those working in computational chemistry/machine learning wishing to seek new application areas.
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