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Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839537388 ISBN 13: 9781839537387
Language: English
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Paperback. Condition: Brand New. 408 pages. 9.25x7.50x0.80 inches. In Stock.
Published by Institution of Engineering and Technology, GB, 2023
ISBN 10: 1839537388 ISBN 13: 9781839537387
Language: English
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Add to basketHardback. Condition: New. Batteries are a necessary part of a low-emission energy system, as they can store renewable electricity and assist the grid. Utility-scale batteries, with capacities of several to hundreds of MWh, are particularly important for condominiums, local grid nodes, and EV charging arrays. However, such batteries are expensive and need to be monitored and managed well to maintain capacity and reliability. Artificial intelligence offers a solution for effective monitoring and management of utility-scale batteries. This book systematically describes AI-based technologies for battery state estimation and modeling for utility-scale Li-ion batteries. Chapters cover utility-scale lithium-ion battery system characteristics, AI-based equivalent modeling, parameter identification, state of charge estimation, battery parameter estimation, offer samples and case studies for utility-scale battery operation, and conclude with a summary and prospect for AI-based battery status monitoring. The book provides practical references for the design and application of large-scale lithium-ion battery systems. AI for Status Monitoring of Utility-Scale Batteries is an invaluable resource for researchers in battery RandD, including battery management systems and related power electronics, battery manufacturers, and advanced students.
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Published by Inst of Engineering & Technology, 2023
ISBN 10: 1839537388 ISBN 13: 9781839537387
Language: English
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Published by Elsevier - Health Sciences Division, US, 2021
ISBN 10: 0323904726 ISBN 13: 9780323904728
Language: English
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Paperback. Condition: New. Battery System Modeling provides advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the modeling of state of charge estimation, energy prediction, power evaluation, health estimation, and active control strategies. Using applications alongside practical case studies, each chapter shows the reader how to use the modeling tools provided. Moreover, the chemistry and characteristics are described in detail, with algorithms provided in every chapter. Providing a technical reference on the design and application of Li-ion battery management systems, this book is an ideal reference for researchers involved in batteries and energy storage. Moreover, the step-by-step guidance and comprehensive introduction to the topic makes it accessible to audiences of all levels, from experienced engineers to graduates.
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