Abstract As one of the most important energy storage devices, lithium-ion (Li-ion) batteries has been widely used. Accurate and robust state of charge (SOC) estimation of lithium-ion battery is a challenging task in battery management system. In this paper, based on the recurrent neural network with gated recurrent unit (Li-ion), a new hybird model is proposed for SOC estimation. Huber-M estimation is used to improve the robustness of traditional Kalman filter and the output of the GRU-RNN is utilized as the observation of the improved Kalman filter. The performance of proposed methods is evaluated by two experimental datasets. We demonstrate the proposed method achieves satisfactory performance, as well as performs strong robustness against influence of measurement errors and outliers.
Li Chaoran,Xiao Fei,Fan Yaxiang等. A Hybrid Approach to Lithium-Ion Battery SOC Estimation Based on Recurrent Neural Network with Gated Recurrent Unit and Huber-M Robust Kalman Filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
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