Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

Predicting, monitoring, and optimizing the performance and health of a battery system entails a variety of complex variables as well as unpredictability in given conditions. Data-driven strategies are crucial for enhancing battery discovery, optimization, and problem solving since current experiments, simulations, and characterization methodologies for nonlinear electrochemical processes are only partially applicable. This review presents a concise compilation of the advanced developments that have taken place in the field of data-driven methodologies concerning batteries. Specifically, important issues related to the cooperation between data-driven approaches and various theoretical strategies, experimental methods, models, characterization tools, and electrochemical performance tests in batteries are discussed. Besides, the application of data-driven methods in all stages of the battery lifecycle, from the design, manufacture, and long-term use stages to the processes of ultimate reuse and recycling, is elaborated. Extraction of data from large datasets and model framework deployment are also covered. Finally, several scientific concerns and possible solutions associated with the further industrialization of data-driven laboratory research in battery areas are also highlighted. This paper provides some guidelines for incorporating the data into nearly every aspect of the battery process for next-generation batteries.