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

doi:10.19595/j.cnki.1000-6753.tces.211069

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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%.

Key words： Lithium-ion battery state-of-charge estimation gated recurrent unit empirical mode decomposition

Received: 19 June 2021

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TM912

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	Articles by authors
	Li Ning
	He Fuxing
	Ma Wentao
	Jiang Lin
	Zhang Xiaoping

Cite this article:

Li Ning,He Fuxing,Ma Wentao等. State-of-Charge Estimation of Lithium-Ion Battery Based on Gated Recurrent Unit Using Empirical Mode Decomposition[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211069 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I17/4528

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