Abstract
AbstractBatteries 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.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
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