AI for Status Monitoring of Utility Scale Batteries

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.