LEADER 03102nam 22005653 450 001 9911007155703321 005 20240930173838.0 010 $a1-83724-508-8 010 $a1-5231-5354-7 010 $a1-83953-739-6 035 $a(CKB)4100000012898012 035 $a(MiAaPQ)EBC30294627 035 $a(Au-PeEL)EBL30294627 035 $a(NjHacI)994100000012898012 035 $a(BIP)085020961 035 $a(OCoLC)1356002705 035 $a(Exl-AI)30294627 035 $a(EXLCZ)994100000012898012 100 $a20221220d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI for Status Monitoring of Utility Scale Batteries 205 $a1st ed. 210 1$aStevenage :$cInstitution of Engineering & Technology,$d2023. 210 4$d©2022. 215 $a1 online resource (385 pages) 225 1 $aEnergy Engineering 311 08$a1-83953-738-8 327 $aCover -- Halftitle Page -- Series Page -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- List of contributors -- 1 Introduction -- 1.1 Motivation for utility-scale battery deployment -- 1.2 Definition of AI in the context of battery management -- 1.3 Advantages of using AI for battery management -- 2 Utility-­scale lithium-­ion battery system characteristics -- 2.1 Overview of lithium-ion batteries -- 2.1.1 Battery working principle -- 2.1.2 Principles of status monitoring of utility-scale batteries -- 2.2 Lithium-ion batteries -- 2.2.1 Lithium iron phosphate batteries -- 2.2.2 Lithium cobaltate oxide batteries -- 2.2.3 Lithium manganese oxide batteries -- 2.3 Large capacity lithium-ion batteries -- 2.3.1 Application areas of utility-scale batteries -- 2.3.2 Characteristics of utility-scale battery systems -- 2.3.3 Operational challenges of utility-scale battery systems -- 3 AI-­based equivalent modeling and parameter identification -- 3.1 Overview of battery equivalent circuit modeling -- 3.2 Modeling types and concepts -- 3.3 Equivalent circuit modeling methods$7Generated by AI. 330 $aUtility-scale Li-ion batteries are poised to play key roles for the clean energy system, but their failure has severe effects. AI can help with their monitoring and management. This work covers machine learning, neural networks, and deep learning, for battery modeling. 410 0$aEnergy Engineering 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aWang$b Shunli$01424658 701 $aLiu$b Kailong$01237966 701 $aWang$b Yujie$01237967 701 $aStroe$b Daniel-Ioan$01785152 701 $aFerna?ndez$b Carlos$c(Lecturer in Analytical Chemistry)$01824899 701 $aGuerrero$b Josep M$01275734 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007155703321 996 $aAI for Status Monitoring of Utility Scale Batteries$94392314 997 $aUNINA