Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective

Shuangqi Li, Hongwen He, Jianwei Li, Peng Yin, Hanxiao Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.
Original languageEnglish
Title of host publication2019 3rd Conference on Vehicle Control and Intelligence (CVCI)
PublisherIEEE
ISBN (Print)9781728126838
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes
Event3rd Conference on Vehicle Control and Intelligence - Hefei, China
Duration: 21 Sep 201922 Sep 2019

Conference

Conference3rd Conference on Vehicle Control and Intelligence
Abbreviated titleCVCI 2019
CountryChina
CityHefei
Period21/09/1922/09/19

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  • Cite this

    Li, S., He, H., Li, J., Yin, P., & Wang, H. (2019). Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective. In 2019 3rd Conference on Vehicle Control and Intelligence (CVCI) IEEE. https://doi.org/10.1109/cvci47823.2019.8951635