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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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    1. Tao, Zihan & Zhang, Chu & Xiong, Jinlin & Hu, Haowen & Ji, Jie & Peng, Tian & Nazir, Muhammad Shahzad, 2023. "Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC," Applied Energy, Elsevier, vol. 336(C).
    2. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    3. Zhang, Chu & Hu, Haowen & Ji, Jie & Liu, Kang & Xia, Xin & Nazir, Muhammad Shahzad & Peng, Tian, 2023. "An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC," Applied Energy, Elsevier, vol. 330(PA).
    4. Luo, Hongyuan & Wang, Deyun & Cheng, Jinhua & Wu, Qiaosheng, 2022. "Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction," Resources Policy, Elsevier, vol. 79(C).
    5. Suo, Leiming & Peng, Tian & Song, Shihao & Zhang, Chu & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad, 2023. "Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm," Energy, Elsevier, vol. 276(C).
    6. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
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