Cluster analysis of echelon utilization of power battery based on machine learning

Hong Li; Heng Jie Li; Jiang Hao Zhu

doi:10.1117/12.2620666

29 November 2021 Cluster analysis of echelon utilization of power battery based on machine learning

Hong Li, Heng Jie Li, Jiang Hao Zhu

Proceedings Volume 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021); 120800W (2021) https://doi.org/10.1117/12.2620666
Event: 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 2021, Nanchang, China

Abstract

There are many factors affecting the consistency of power battery. Aiming at the problems of poor consistency and inaccurate clustering reorganization of retired power batteries, this paper mainly considers three characteristic parameters: capacity, voltage and internal resistance, and proposes a clustering reorganization method of retired power batteries based on machine learning. Firstly, the article based on the characteristic parameters of retired batteries, analysed its health status and service life and through machine learning, according to the actual requirements of groups, clustered and reorganized the batteries and its accuracy is analysed; Then the article analysed the applicability of power battery echelon utilization scenario.

Citation Download Citation

Hong Li, Heng Jie Li, and Jiang Hao Zhu "Cluster analysis of echelon utilization of power battery based on machine learning", Proc. SPIE 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 120800W (29 November 2021); https://doi.org/10.1117/12.2620666

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