Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Abstract Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data‐driven artificial intelligence systems. This review provides a unique perspective on recent progress in data‐driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high‐throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics‐informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics‐informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data‐driven characterization and prognosis are discussed toward accelerating energy device development with much‐enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next‐generation battery development.