基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法

doi:10.19595/j.cnki.1000-6753.tces.190547

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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.

Key words： Lithium-ion battery state of charge recurrent neural network with gated recurrent unit Kalman filter

Received: 07 May 2019 Published: 12 May 2020

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TM912

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	Li Chaoran
	Xiao Fei
	Fan Yaxiang
	Yang Guorun
	Tang Xin

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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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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190547 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I9/2051

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