Research Article
State-of-Health Estimation of Lithium-ion Battery Based on Data-driven
@INPROCEEDINGS{10.4108/eai.17-6-2022.2322708, author={Jingyi Gao and Ping Li and Yisu Yan and Mengjie Guo and Kai Wang}, title={State-of-Health Estimation of Lithium-ion Battery Based on Data-driven}, proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2022}, month={10}, keywords={state-of-health; lithium-ion batteries; machine learning; data-driven model}, doi={10.4108/eai.17-6-2022.2322708} }- Jingyi Gao
Ping Li
Yisu Yan
Mengjie Guo
Kai Wang
Year: 2022
State-of-Health Estimation of Lithium-ion Battery Based on Data-driven
ICIDC
EAI
DOI: 10.4108/eai.17-6-2022.2322708
Abstract
With the increase of the output of electric vehicles, it is of great significance to predict the health status of lithium-ion batteries for the safe operation of electric vehicles. In this paper, some common data-driven methods for health state estimation of lithium-ion batteries are reviewed. First of all, this paper introduces the charge and discharge principle of lithium-ion battery. Then four common SOH prediction methods are introduced, and their advantages and disadvantages are summarized and reviewed. In the part of introducing the data-driven research on the health status of lithium-ion battery, it focuses on the application of machine learning and deep neural network. Finally, the research prospect of health state estimation of lithium-ion battery is explained.
