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

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 neura...

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Veröffentlicht in:Energy (Oxford) Jg. 227; S. 120451
Hauptverfasser: Chen, Junxiong, Feng, Xiong, Jiang, Lin, Zhu, Qiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 15.07.2021
Elsevier BV
Schlagworte:
Data processing
Denoising autoencoder
Electric vehicle
energy
extracts
Feature extraction
Gated recurrent unit
gates
information processing
Lithium
lithium batteries
Lithium-ion batteries
Lithium-ion battery
Neural networks
Noise measurement
Noise reduction
processing technology
Rechargeable batteries
Recurrent neural network
Recurrent neural networks
State of charge
State of charge estimation
Lithium-ion battery
Gated recurrent unit
Denoising autoencoder
State of charge estimation
Electric vehicle
Recurrent neural network
ISSN:0360-5442, 1873-6785
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Zusammenfassung: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.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.120451