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010   $a981-9925-24-X
024 7 $a10.1007/978-981-99-2524-7
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035   $a(Au-PeEL)EBL30722843
035   $a(DE-He213)978-981-99-2524-7
035   $a(PPN)272267643
035   $a(EXLCZ)9928100244700041
100   $a20230828d2023      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aNew Paradigms in Flow Battery Modelling$b[electronic resource] /$fby Akeel A. Shah, Puiki Leung, Qian Xu, Pang-Chieh Sui, Wei Xing
205   $a1st ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (389 pages)
225 1 $aEngineering Applications of Computational Methods,$x2662-3374 ;$v16
311 08$aPrint version: A. Shah, Akeel New Paradigms in Flow Battery Modelling Singapore : Springer,c2023 9789819925230 
327   $aChapter 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.
330   $aThis book provides a comprehensive review of the latest modelling developments in flow batteries, as well as some new results and insights. Flow batteries have long been considered the most flexible answer to grid scale energy storage, and modelling is a key component in their development. Recent modelling has moved beyond macroscopic methods, towards mesoscopic and smaller scales to select materials and design components. This is important for both fundamental understanding and the design of new electrode, catalyst and electrolyte materials. There has also been a recent explosion in interest in machine learning for electrochemical energy technologies. The scope of the book includes these latest developments and is focused on advanced techniques, rather than traditional modelling paradigms. The aim of this book is to introduce these concepts and methods to flow battery researcher, but the book would have a much broader appeal since these methods also employed in other battery and fuel cell systems and far beyond. The methods will be described in detail (necessary fundamental material in Appendices). The book appeals to graduate students and researchers in academia/industry working in electrochemical systems, or those working in computational chemistry/machine learning wishing to seek new application areas. .
410  0$aEngineering Applications of Computational Methods,$x2662-3374 ;$v16
606   $aElectric batteries
606   $aMaterials
606   $aComputer simulation
606   $aFuel cells
606   $aMathematical physics
606   $aBatteries
606   $aComputer Modelling
606   $aFuel Cells
606   $aComputational Physics and Simulations
615  0$aElectric batteries.
615  0$aMaterials.
615  0$aComputer simulation.
615  0$aFuel cells.
615  0$aMathematical physics.
615 14$aBatteries.
615 24$aComputer Modelling.
615 24$aFuel Cells.
615 24$aComputational Physics and Simulations.
676   $a620.11
676   $a621.31242
700   $aA. Shah$b Akeel$01425683
701   $aLeung$b Puiki$01425684
701   $aXu$b Qian$01059469
701   $aSui$b Pang-Chieh$01425685
701   $aXing$b Wei$01192001
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910742498403321
996   $aNew Paradigms in Flow Battery Modelling$93556391
997   $aUNINA