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

Author

Listed:
  • Zhengyi Bao

    (School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Jiahao Jiang

    (School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Chunxiang Zhu

    (School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
    Engineering Training Center, China Jiliang University, Hangzhou 310018, China)

  • Mingyu Gao

    (School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China
    Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China)

Abstract

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.

Suggested Citation

  • Zhengyi Bao & Jiahao Jiang & Chunxiang Zhu & Mingyu Gao, 2022. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 15(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4399-:d:840636
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    References listed on IDEAS

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    1. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
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    4. Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.
    5. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(C).
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    Cited by:

    1. Erwin Sutanto & Putu Eka Astawa & Fahmi Fahmi & Muhammad Imran Hamid & Muhammad Yazid & Wervyan Shalannanda & Muhammad Aziz, 2023. "Lithium-Ion Battery State-of-Charge Estimation from the Voltage Discharge Profile Using Gradient Vector and Support Vector Machine," Energies, MDPI, vol. 16(3), pages 1-20, January.
    2. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost," Energies, MDPI, vol. 15(16), pages 1-18, August.
    3. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.

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