Batteries are a necessary part of a low-emission energy system, as they can store renewable electricity and assist the grid. Particularly utility-scale batteries, with capacities of several to hundreds of MWh, are important for e.g. condominiums, local grid nodes and EV charging arrays. But they are expensive and need to be monitored and managed well in order to maintain capacity and reliability. Artificial intelligence is key to this.

This book systematically describes AI-based technologies for battery state estimation and modelling for utility-scale Li-ion batteries. It covers AI methods for circuit modelling, parameter identification, state of charge estimation, state of health evaluation, state of power determination, state of energy calculation, and remaining useful life prediction. The AI methods include machine learning, artificial neural networks, and deep learning. The book provides practical references for the design and application of large-scale lithium-ion battery systems. Examples are given to serve as references.