AI for Status Monitoring of Utility Scale Batteries

• Autore: Wang Shunli
• Pubblicazione: Stevenage : , : Institution of Engineering & Technology, , 2023; ©2022
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (385 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning
• Altri autori: LiuKailong WangYujie StroeDaniel-Ioan FernándezCarlos (Lecturer in Analytical Chemistry) GuerreroJosep M
• Nota di contenuto: Cover -- 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
• Sommario/riassunto: Utility-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.
• Titolo autorizzato: AI for Status Monitoring of Utility Scale Batteries
• ISBN: 1-83724-508-8; 1-5231-5354-7; 1-83953-739-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911007155703321
• Lo trovi qui: Univ. Federico II
• Serie: Energy Engineering