BATTERY CONSISTENCY DIAGNOSIS BASED ON EVIDENTIAL KNN CLASSIFIER

Wang Nan; Zhou Xichao; Peng Yong; Li Zhen; An Kun; Zhao Pengxiang

doi:10.19912/j.0254-0096.tynxb.2022-0023

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 13-19. DOI: 10.19912/j.0254-0096.tynxb.2022-0023

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

BATTERY CONSISTENCY DIAGNOSIS BASED ON EVIDENTIAL KNN CLASSIFIER

Wang Nan, Zhou Xichao, Peng Yong, Li Zhen, An Kun, Zhao Pengxiang

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Abstract

The inconsistency of battery unit inevitably occurs in the long-term operation of the large-capacity energy storage stations composed by series-parallel connection. Therefore, timely identification of battery units with potential abnormalities can not only provide data support for energy storage stations to assess the health status of batteries, but also reduce the systemic operation risks. In this paper, we proposes a consistency diagnosis method for energy storage batteries based on evidential KNN classifier, which is independent of battery model and empirical data. Concretely, this method is based on a large number of single unit voltage and temperature operation data in a cluster of units in an energy storage stations, and uses an evidential KNN classifier to construct a diagnostic model that can characterize the consistency of unit voltage and temperature, and accurately identifies abnormal single units through an anomaly inversion algorithm.

Key words

energy storage battery / consistency diagnosis / evidential KNN classifier / anomaly inversion algorithm

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Wang Nan, Zhou Xichao, Peng Yong, Li Zhen, An Kun, Zhao Pengxiang. BATTERY CONSISTENCY DIAGNOSIS BASED ON EVIDENTIAL KNN CLASSIFIER[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 13-19 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0023

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