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AI for Status Monitoring of Utility Scale Batteries

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Publication Year: 2022

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 R&D, including battery management systems and related power electronics, battery manufacturers, and advanced students.

Inspec keywords: battery management systems; power engineering computing; artificial intelligence; secondary cells; lithium compounds

Other keywords: wind power plants; battery management systems; parameter estimation; distributed power generation; fossil fuels; utility scale batteries; power engineering computing; artificial intelligence; lithium compounds; status monitoring; secondary cells; photovoltaic power systems

Subjects: Secondary cells; General electrical engineering topics; General and management topics; Conference proceedings; Secondary cells; Knowledge based systems; Power engineering computing

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