A review of the recent progress in battery informatics

Abstract

Abstract Batteries are of paramount importance for the energy storage, consumption, and transportation in the current and future society. Recently machine learning (ML) has demonstrated success for improving lithium-ion technologies and beyond. This in-depth review aims to provide state-of-art achievements in the interdisciplinary field of ML and battery research and engineering, the battery informatics. We highlight a crucial hurdle in battery informatics, the availability of battery data, and explain the mitigation of the data scarcity challenge with a detailed review of recent achievements. This review is concluded with a perspective in this new but exciting field.

Topics & Concepts