State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network - Chen, Junxiong; Feng, Xiong; Jiang, Lin; Zhu, Qiao (Energy (Oxford), 2021)
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. •A combined neural network method is proposed for the SOC estimation.•A DAE-NN is introduced to extract the useful battery data features.•A GRU-RNN is used to achieve the SOC estimation using the feature-extracted data.•The method exhibits accurate SOC estimation and excellent generalization ability.