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

被引:24
作者
Ji, Shanling [1 ]
Zhu, Jianxiong [1 ,2 ]
Yang, Yaxin [1 ]
dos Reis, Goncalo [3 ]
Zhang, Zhisheng [1 ]
机构
[1] Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China
[2] Chinese Acad Sci, Shanghai Inst Tech Phys, State Key Lab Infrared Phys, Shanghai 200083, Peoples R China
[3] Univ Edinburgh, Sch Math, JCMB, Peter Guthrie Tait Rd, Edinburgh EH9 3FD, Midlothian, Scotland
关键词
battery characterization; battery prognosis; data-driven methods; explainable artificial intelligence; physics-informed learning; LITHIUM-ION BATTERY; INFORMED NEURAL-NETWORK; VOLTAGE FAULT-DIAGNOSIS; CHARGE ESTIMATION; LIFETIME PREDICTION; STATE; SYSTEMS; ENTROPY; MODEL; MANAGEMENT;
D O I
10.1002/smtd.202301021
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
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. The data-driven characterization and prognosis methods for batteries, including multiscale informative characterization and physics-informed machine learning developed in recent years, are reviewed in this article. This review proposes promising research directions of multimodal fusion and unified modeling for accelerating next-generation battery development.image
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页数:17
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