New Paradigms in Flow Battery Modelling - A. Shah, Akeel

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Résumé :
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. ...

Biographie:
Professor Akeel Shah graduated with a first-class honours degree in Mathematical Physics in 1995 and a Ph.D. in Applied Mathematics (both from University of Manchester Institute of Science and Technology) in 2001. He is currently a professor in the School of Energy and Power Engineering at the Chongqing University, China, with expertise in electrochemical energy conversion, computational engineering and applied machine learning. He previously held positions at University of Southampton and University of Warwick. His work is primarily focused on the modelling and simulation of energy conversion devices, including computational modelling, and the development of fast algorithms for computer codes in science and engineering based on machine learning and computational statistics. Between 2004 and 2006, he held a joint Pacific Institute of Mathematics Sciences (PIMS) and Mathematics of Information Technology and Complex Systems (MITACS) Fellowship. He is the author of over 85 publicationsin leading international peer-reviewed journals. Professor Shah has worked closely with the fuel cell and battery industry (Ballard Power Systems, Johnson Matthey Plc, Sharp Laboratories, ACAL Energy Ltd) to develop models/numerical codes for design purposes. He has received funding from the TSB, FP7, DSTL and directly from industry.