Machine learning of materials design and state prediction for lithium ion batteries
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 techn...
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| Published in | Chinese journal of chemical engineering Vol. 37; no. 9; pp. 1 - 11 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2021
State Key Laboratory of Chemical Engineering,Institute of Pharmaceutical Engineering,College of Chemical and Biological Engineering,Zhejiang University,Hangzhou 310027,China%State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China |
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| 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. |
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| AbstractList | 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 improve-ment of lithium-ion batteries. 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. |
| Author | Mao, Jiale Miao, Jiazhi Tong, Zheming Lu, Yingying |
| AuthorAffiliation | State Key Laboratory of Chemical Engineering,Institute of Pharmaceutical Engineering,College of Chemical and Biological Engineering,Zhejiang University,Hangzhou 310027,China%State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China |
| AuthorAffiliation_xml | – name: State Key Laboratory of Chemical Engineering,Institute of Pharmaceutical Engineering,College of Chemical and Biological Engineering,Zhejiang University,Hangzhou 310027,China%State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China |
| Author_xml | – sequence: 1 givenname: Jiale surname: Mao fullname: Mao, Jiale organization: State Key Laboratory of Chemical Engineering, Institute of Pharmaceutical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China – sequence: 2 givenname: Jiazhi orcidid: 0000-0002-9739-9008 surname: Miao fullname: Miao, Jiazhi organization: State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China – sequence: 3 givenname: Yingying surname: Lu fullname: Lu, Yingying email: yingyinglu@zju.edu.cn organization: State Key Laboratory of Chemical Engineering, Institute of Pharmaceutical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China – sequence: 4 givenname: Zheming surname: Tong fullname: Tong, Zheming email: tzm@zju.edu.cn organization: State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China |
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| ContentType | Journal Article |
| Copyright | 2021 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: 2021 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | Lithium ion batteries Materials design State prediction Machine learning |
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| Publisher | Elsevier B.V State Key Laboratory of Chemical Engineering,Institute of Pharmaceutical Engineering,College of Chemical and Biological Engineering,Zhejiang University,Hangzhou 310027,China%State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China |
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