Machine Learning for Advanced Battery Systems

Machine learning has significant potential for enabling a more economic, efficient, and reliable low-carbon transition of energy systems, such as improving generation and load forecasting, accelerating the design of next-generation battery chemistries, enhancing distributed energy resource coordination, and advancing battery management systems, including battery lifetime prediction, capacity fade estimation, and optimal charge design. The purpose of this Special Issue is to provide an overview of the state-of-the-art, to present new research results and to discuss the promising future research directions at the interface between energy and machine learning.