State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network

Junxiong Chen, Xiong Feng, Lin Jiang and Qiao Zhu

Energy, 2021, vol. 227, issue C

Abstract: To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conventional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neural network (DAE-NN) is introduced to extract the useful data features by reducing the noise and increasing the dimensions of the battery measurement data. Then, the feature-extracted data is utilized to train the GRU-RNN, which is widely used in SOC estimation. Notice that a good input data processing method plays a key role in the SOC estimation performance and the generalization ability. Therefore, it is not trivial to combine the data processing method (DAE-NN), and the SOC estimation method (GRU-RNN), which is named DAE-GRU. Compared with the traditional GRU-RNN, the new DAE-GRU method shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the DAE-NN. Finally, three different driving cycles are given in the experiment to cross-train and verify the proposed DAE-GRU, GRU-RNN and RNN. Compared with the GRU-RNN and the RNN, it is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation.

Keywords: State of charge estimation; Lithium-ion battery; Denoising autoencoder; Gated recurrent unit; Recurrent neural network; Electric vehicle (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007003

DOI: 10.1016/j.energy.2021.120451

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