Voltage difference over-limit fault prediction of energy storage battery cluster based on data driven method

Article type: Research Article

Authors: Zhao, Weisen^{a; b; *}

Affiliations: [a] School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China | [b] Energy Storage and Electrotechnics Department, China Electric Power Research Institute, Beijing, China

Correspondence: [*] Corresponding author. Weisen Zhao, E-mail: [email protected].

Abstract: Electrochemical energy storage battery fault prediction and diagnosis can provide timely feedback and accurate judgment for the battery management system(BMS), so that this enables timely adoption of appropriate measures to rectify the faults, thereby ensuring the long-term operation and high efficiency of the energy storage battery system. Based on the idea of data driven, this paper applies the Long-Short Term Memory(LSTM) algorithm in the field of artificial intelligence to establish the fault prediction model of energy storage battery, which can realize the prediction of the voltage difference over-limit fault according to the operation data of the energy storage battery, and introduce the parameter of the difference between maximum voltage and minimum voltage(DMM) at the cluster level to quantitatively determine whether the battery cluster has a fault. It provides powerful guidance and effective methods for the safe and stable operation of electrochemical energy storage power stations.

Keywords: Fault prediction, data driven, LSTM, artificial intelligence

DOI: 10.3233/JIFS-235726

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5155-5164, 2024