Machine learning of materials design and state prediction for lithium ion batteries

doi:10.1016/j.cjche.2021.04.009

中国化学工程学报 ›› 2021, Vol. 37 ›› Issue (9): 1-11.DOI: 10.1016/j.cjche.2021.04.009

• Reviews • 下一篇

Machine learning of materials design and state prediction for lithium ion batteries

Jiale Mao¹, Jiazhi Miao², Yingying Lu¹, Zheming Tong²

1. State Key Laboratory of Chemical Engineering, Institute of Pharmaceutical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China;
2. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

收稿日期:2021-02-24 修回日期:2021-04-19 出版日期:2021-09-28 发布日期:2021-11-02
通讯作者: Yingying Lu, Zheming Tong
基金资助:
We acknowledge financial supports from the National Key Research and Development Program of China (2018YFA0209600) and the Natural Science Foundation of China (22022813, 21878268, 52075481).

Machine learning of materials design and state prediction for lithium ion batteries

Jiale Mao¹, Jiazhi Miao², Yingying Lu¹, Zheming Tong²

1. State Key Laboratory of Chemical Engineering, Institute of Pharmaceutical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China;
2. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

Received:2021-02-24 Revised:2021-04-19 Online:2021-09-28 Published:2021-11-02
Contact: Yingying Lu, Zheming Tong
Supported by:
We acknowledge financial supports from the National Key Research and Development Program of China (2018YFA0209600) and the Natural Science Foundation of China (22022813, 21878268, 52075481).

摘要/Abstract

摘要： With the widespread use of lithium ion batteries in portable electronics and electric vehicles, further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers. Machine learning, one of the core technologies of artificial intelligence, is rapidly changing many fields with its ability to learn from historical data and solve complex tasks, and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries. This review begins with the introduction of the conceptual framework of machine learning and the general process of its application, then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state, and finally points out the current application problems of machine learning and future research directions. It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

关键词: Lithium ion batteries, Machine learning, Materials design, State prediction

Abstract: With the widespread use of lithium ion batteries in portable electronics and electric vehicles, further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers. Machine learning, one of the core technologies of artificial intelligence, is rapidly changing many fields with its ability to learn from historical data and solve complex tasks, and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries. This review begins with the introduction of the conceptual framework of machine learning and the general process of its application, then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state, and finally points out the current application problems of machine learning and future research directions. It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

Key words: Lithium ion batteries, Machine learning, Materials design, State prediction

Jiale Mao, Jiazhi Miao, Yingying Lu, Zheming Tong. Machine learning of materials design and state prediction for lithium ion batteries[J]. 中国化学工程学报, 2021, 37(9): 1-11.

Jiale Mao, Jiazhi Miao, Yingying Lu, Zheming Tong. Machine learning of materials design and state prediction for lithium ion batteries[J]. Chinese Journal of Chemical Engineering, 2021, 37(9): 1-11.

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Machine learning of materials design and state prediction for lithium ion batteries

Machine learning of materials design and state prediction for lithium ion batteries

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