State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks

Huaqin Zhang, Jichao Hong (), Zhezhe Wang and Guodong Wu
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Huaqin Zhang: Key Laboratory of Conveyance Equipment, Ministry of Education, East China Jiaotong University, Nanchang 330013, China
Jichao Hong: Key Laboratory of Conveyance Equipment, Ministry of Education, East China Jiaotong University, Nanchang 330013, China
Zhezhe Wang: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Guodong Wu: Key Laboratory of Conveyance Equipment, Ministry of Education, East China Jiaotong University, Nanchang 330013, China

Energies, 2022, vol. 15, issue 22, 1-14

Abstract: Multiple faults in new energy vehicle batteries can be diagnosed using voltage. To find voltage fault information in advance and reduce battery safety risk, a state-partitioned voltage fault prognosis method based on the self-attention network is proposed. The voltage data are divided into three parts with typical characteristics according to the charging voltage curve trends under different charge states. Subsequently, a voltage prediction model based on the self-attention network is trained separately with each part of the data. The voltage fault prognosis is realized using the threshold method. The effectiveness of the method is verified using real operating data of electric vehicles (EVs). The effects of different batch sizes and window sizes on model training are analyzed, and the optimized hyperparameters are used to train the voltage prediction model. The average error of predicted voltage is less than 2 mV. Finally, the superiority and robustness of the method are verified.

Keywords: electric vehicles; battery systems; self-attention mechanism; voltage prediction; fault prognosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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