State-of-Charge Estimation of Lithium-Ion Battery Based on Gated Recurrent Unit Using Empirical Mode Decomposition
Li Ning1, He Fuxing1, Ma Wentao1, Jiang Lin2, Zhang Xiaoping3
1. School of Electrical Engineering Xi'an University of Technology Xi'an 710048 China; 2. Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom; 3. Department of Electronics, Electrical and Systems Engineering University of Birmingham Birmingham B15 2TT United Kingdom
Abstract：State of charge (SOC) estimation technology of lithium-ion battery is an important part of the battery management system (BMS) design of electric vehicles. In this paper, an SOC estimation method for lithium-ion batteries based on gated recurrent unit (GRU) using empirical mode decomposition (EMD) is proposed. The EMD algorithm is introduced to decompose the discharge current, based on the GRU estimation of the SOC, which not only improves the ability of the GRU model to maintain long-term information for long-term current signals, but also betters the accuracy of SOC estimation of lithium-ion battery. Simulation experiments show that, compared with the traditional recurrent neural network and long-term and short-term memory network, the EMD-GRU method proposed in this paper displays the average absolute error of the lithium-ion battery SOC estimation is 1.509 3%, a year-on-year decrease of 20.792 4%.
李宁, 何复兴, 马文涛, 蒋林, 张小平. 基于经验模态分解的门控循环单元神经网络的锂离子电池荷电状态估计[J]. 电工技术学报, 2022, 37(17): 4528-4536.
Li Ning, He Fuxing, Ma Wentao, Jiang Lin, Zhang Xiaoping. State-of-Charge Estimation of Lithium-Ion Battery Based on Gated Recurrent Unit Using Empirical Mode Decomposition. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536.
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2022, Vol. 37 
