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Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training

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  • Zhao, Hongqian
  • Chen, Zheng
  • Shu, Xing
  • Shen, Jiangwei
  • Liu, Yonggang
  • Zhang, Yuanjian

Abstract

Accurate and early detection of voltage faults facilitates the driver and battery management system to take protective measures and reduce property damage and passenger injury. To identify the battery operation fault in a timely manner, this study develops an accurate multi-step voltage prediction and voltage fault diagnosis method based on gated recurrent unit neural network and incremental training. First, considering the impacts of drivers’ behaviors and vehicle states on battery performance under practical operations, a long-term operation dataset of electric scooters is acquired and established, and the Pearson correlation coefficient is applied to quantify these correlations. Then, the gated recurrent unit neural network, together with the multi-step ahead prediction scheme, is advanced to construct the voltage prediction model. Next, to effectively capture the performance variation of battery under complex dynamic operating environment, the incremental learning approach is developed to adaptively update the prediction model. Finally, the fault diagnosis strategy is proposed, with the combination of the voltage prediction model, to accurately detect battery faults of over-voltage, under-voltage, over-voltage change rate and poor consistency. The experimental validations highlight that the proposed method can predict the battery voltage 1 min in advance, and detect battery faults in real time with high accuracy.

Suggested Citation

  • Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033825
    DOI: 10.1016/j.energy.2022.126496
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    References listed on IDEAS

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    2. Yao, Lei & Dai, Huilin & Xiao, Yanqiu & Zhao, Changsheng & Fei, Zhigen & Cui, Guangzhen & Zhang, Longhai, 2024. "An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making," Energy, Elsevier, vol. 306(C).
    3. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    4. Dong, Chenchen & Sun, Dashuai, 2024. "Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    5. Shen, Jiangwei & Ma, Wensai & Shu, Xing & Shen, Shiquan & Chen, Zheng & Liu, Yonggang, 2023. "Accurate state of health estimation for lithium-ion batteries under random charging scenarios," Energy, Elsevier, vol. 279(C).
    6. Ren, Song & Sun, Jing, 2024. "Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system," Energy, Elsevier, vol. 292(C).
    7. Li, Da & Zhang, Lei & Zhang, Zhaosheng & Liu, Peng & Deng, Junjun & Wang, Qiushi & Wang, Zhenpo, 2023. "Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality," Energy, Elsevier, vol. 284(C).
    8. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).

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