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Article

A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

1
School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2
Engineering Training Center, China Jiliang University, Hangzhou 310018, China
3
Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Energies 2022, 15(12), 4399; https://doi.org/10.3390/en15124399
Received: 24 May 2022 / Revised: 13 June 2022 / Accepted: 15 June 2022 / Published: 16 June 2022
Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset. View Full-Text
Keywords: lithium-ion battery; state-of-health; dilated convolutional neural networks; bidirectional gated recurrent units; hybrid network lithium-ion battery; state-of-health; dilated convolutional neural networks; bidirectional gated recurrent units; hybrid network
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MDPI and ACS Style

Bao, Z.; Jiang, J.; Zhu, C.; Gao, M. A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery. Energies 2022, 15, 4399. https://doi.org/10.3390/en15124399

AMA Style

Bao Z, Jiang J, Zhu C, Gao M. A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery. Energies. 2022; 15(12):4399. https://doi.org/10.3390/en15124399

Chicago/Turabian Style

Bao, Zhengyi, Jiahao Jiang, Chunxiang Zhu, and Mingyu Gao. 2022. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery" Energies 15, no. 12: 4399. https://doi.org/10.3390/en15124399

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