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A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)

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  • Korkmaz, Mehmet

Abstract

Deep learning-based (DL) methods have recently come to the forefront among the data-driven models due to their success in capturing the complexities of the battery. Many previous DL-based studies for SoC estimation have almost exclusively focused on improving DL structure by proposing various architectures. Questions regarding the outlier or atypical predictions have yet to be adequately addressed. Furthermore, few works benefit from optimization algorithms to determine the hyperparameters of DL. In this study, we have addressed the problem of how to obtain the hyperparameter of DL and fix the improper DL predictions. To this aim, we used two different optimization algorithms to determine the hyperparameters of DL and proposed a novel algorithm that considers the previous SoC estimations. The algorithm either approves or rejects the DL predictions for the relevant step and offers new values for the rejected ones. The proposed scheme is evaluated using a battery dataset which includes different driving cycles. According to the results, it is observed that the optimized DL outperforms the empirical one by at least 35% in terms of performance indices. Moreover, the proposed novel algorithm successfully integrates into all variations and significantly improves the performance index scores.

Suggested Citation

  • Korkmaz, Mehmet, 2024. "A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)," Energy, Elsevier, vol. 294(C).
  • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006856
    DOI: 10.1016/j.energy.2024.130913
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    References listed on IDEAS

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    1. Wen-Jing Shen & Han-Xiong Li, 2017. "Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm," Energies, MDPI, vol. 10(4), pages 1-18, March.
    2. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    4. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    5. Longxing Wu & Kai Liu & Hui Pang & Jiamin Jin, 2021. "Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias," Energies, MDPI, vol. 14(17), pages 1-12, August.
    6. Chen, Junxiong & Feng, Xiong & Jiang, Lin & Zhu, Qiao, 2021. "State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network," Energy, Elsevier, vol. 227(C).
    7. Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
    8. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    9. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    10. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
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