Physics-Informed Artificial Intelligence for Battery Energy Storage Systems

Dear Colleagues,

In order to meet the demand of the energy revolution for carbon neutralization, batteries have become one of the most important components in all modes of electrified transportation and the supporting electric energy stations. The performance of a battery energy storage system affects the efficiency and safety of the operation of a power system significantly. Despite the widespread use of traditional modeling mechanisms and state estimation methods for battery energy storage systems, machine learning, physics-informed knowledge, and intelligent control have attracted increasing attention recently.

Therefore, this Special Issue is focused on recent advances in battery energy storage systems that address the above-mentioned aspects and go beyond the state of the art. Prospective authors are invited to submit original contributions/articles for review and possible publication in this Special Issue. Topics of interest include (but are not limited to) the following:

• The design of BMSs (battery management systems);
• Physics-informed and data-driven methods for battery modeling;
• State estimation of battery energy storage systems;
• Innovative methods of SOX (SOC, SOH, SOP, SOE, SOS, SOT) and other states estimations methodologies;
• Intelligent battery management systems with advanced algorithms;
• Battery safety management, including safety diagnostics, prognostics, and warning methods;
• Battery life management, including degradation estimation, aging prediction, and health management;
• Temperature control and thermal management of battery energy storage systems;
• Methods for battery optimization, control, and balancing;
• Energy management of advanced power systems.

Dr. Yanan Wang
Prof. Dr. Liping Chen
Prof. Dr. Yangquan Chen
Guest Editors

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