Online capacity estimation of lithium-ion batteries based on convolutional self-attention

ABSTRACT

It is of great significance to improve the safety, efficiency, and economy of lithium-ion batteries by improving the capacity estimation accuracy of lithium-ion batteries. In this paper, feature extraction and correlation analysis are carried out on the data of lithium-ion battery charging process, and the voltage curve of constant current charging stage is extracted. The difference characteristics between each cycle are used to describe the battery capacity, and these statistical characteristics are proved to be highly correlated with the battery capacity. Furthermore, an online estimation model of battery capacity based on convolution self-attention is established, and the above characteristics of constant current charging process and the battery capacity of the latest cycle are fused as input vectors of the model to realize online estimation of battery capacity. Finally, an open data set is used to verify the model experiment. The results show that the prediction error of MAE is 0.17% and that of RMSE is 0.22%.

KEYWORDS:

• Capacity estimation
• charging voltage
• convolutional self-attention
• transformer
• lithium-ion batteries
• automotive

Acknowledgements

The establishment of the prediction model of this study has been supported by the New Energy Power Research Group of Shanghai University of Technology

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Writing – review and editing, D.Z.; writing – original draft, D.Z. and X.S.; software, D.Z., X.S., Z.Z., and C.Y.; methodology, D.Z., X.S., Z.Z., and C.Y.; investigation, D.Z., X.S., Z.Z., and C.Y.; project administration, D.Z. All authors have read and agreed to the published version of the manuscript.

Additional information

Funding

This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFE0193900, in part by the National Natural Science Foundation of China under Grants 52002286 and in part by the Fundamental Research Funds for the Central Universities. 22120220642.

Notes on contributors

Dekang Zhu

Dekang Zhu, Ph.D. Candidate, College of Electronic and Information Engineering, Tongji University.

Xiaoyu Shen

Xiaoyu Shen, Master Candidate, College of Mechanical Engineering, University of Shanghai for Science and Technology.

Congbo Yin

Congbo Yin, D. degree in Mechanical and Engineering from University of Shanghai for Science and Technology in 2013.

Zhongpan Zhu

Zhongpan Zhu, D. degree in Vehicle Engineering from Tongji University in 2019.