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Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction

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  • Zhang, Chu
  • Zhang, Yue
  • Li, Zhengbo
  • Zhang, Zhao
  • Nazir, Muhammad Shahzad
  • Peng, Tian

Abstract

Accurately estimating the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is crucial for their safe and stable operation. This study proposes a hybrid deep learning model based on Gaussian data augmentation (GDA), the TimesNet model, error correction (EC), and an improved Bayesian algorithm called Sequential Model-based Algorithm Configuration (SMAC) for SOC and SOE estimation in lithium-ion batteries. Firstly, we compared the performance of the TimesNet model with other benchmark models. Then, GDA data with different signal-to-noise ratios were used for testing, and the model's performance was improved using GDA data with appropriate signal-to-noise ratios. Finally, an error correction method was employed to further enhance the estimation accuracy. During the experiment, SMAC was used to optimize its hyperparameters. In NN and UDDS drive cycles at temperatures of 0 °C, 10 °C, and 25 °C, the highest RMSE values for SOC and SOE estimation of the proposed model were 0.105%, 0.098%, 0.227%, and 0.213%, respectively. Experimental results demonstrate that the TimesNet model achieves good prediction performance for SOC and SOE estimation. GDA and EC effectively enhance the accuracy of the model.

Suggested Citation

  • Zhang, Chu & Zhang, Yue & Li, Zhengbo & Zhang, Zhao & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction," Applied Energy, Elsevier, vol. 359(C).
    • Handle: RePEc:eee:appene:v:359:y:2024:i:c:s0306261924000527
    DOI: 10.1016/j.apenergy.2024.122669
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    References listed on IDEAS

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    Cited by:

    1. Wang, Xiaoxuan & Yi, Yingmin & Yuan, Yiwei & Li, Xifei, 2025. "Enhanced state of charge estimation in lithium-ion batteries based on Time-Frequency-Net with time-domain and frequency-domain features," Energy, Elsevier, vol. 318(C).
    2. Liu, Zixi & Ruan, Guanqiang & Tian, Yupeng & Hu, Xing & Yan, Rong & Yang, Kuo, 2024. "A real-world battery state of charge prediction method based on a lightweight mixer architecture," Energy, Elsevier, vol. 311(C).
    3. Ruan, Guanqiang & Liu, Zixi & Cheng, Jinrun & Hu, Xing & Chen, Song & Liu, Shiwen & Guo, Yong & Yang, Kuo, 2024. "A deep learning model for predicting the state of energy in lithium-ion batteries based on magnetic field effects," Energy, Elsevier, vol. 304(C).
    4. Chen, Yuan & Duan, Wenxian & Huang, Xiaohe & Wang, Shunli, 2024. "Multi-output fusion SOC and SOE estimation algorithm based on deep network migration," Energy, Elsevier, vol. 308(C).

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