Corresponding Author

Hao Zhang(dr.h.zhang@hotmail.com)


Lithium-ion batteries (LIBs) have become one of the best solutions to the energy storage issue in modern society. However, the battery materials and device development are both complex, and involve multivariable problems. Traditional trial-and-error approach, which relies on researchers to conduct experiments, has encountered bottlenecks in the improvement of the battery performance. Artificial intelligence (AI) is the most potential technology to deal with this issue due to its powerful high-speed and capabilities of processing massive data. In particular, the capability of machine learning (ML) algorithms in assessing multidimensional data variables and discovering patterns in the sets are expected to assist researchers in discovering patterns and elucidating the mechanisms of material synthesis and device fabrication. This review summarizes various challenges encountered in traditional research methods of LIBs and introduces the applications of AI in battery material research, battery device design and manufacturing, material and device characterizations, and battery cycle life and safety assessment in detail. Most importantly, we present the challenges faced by AI and ML in battery research, and discuss the shortcomings and prospects of their applications. We believe that a closer collaboration among experimentalists, modeling specialists, and AI experts in the future will greatly facilitate AI and ML methods for solving battery and materials problems that are difficult to be solved by traditional methods.

Graphical Abstract


lithium-ion battery, machine learning, materials characterization, battery manufacture, artificial intelligence

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