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Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management

Author

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  • Kailong Liu
  • Kang Li
  • Qiao Peng
  • Yuanjun Guo
  • Li Zhang

Abstract

Temperature is a crucial state to guarantee the reliability and safety of a battery during operation. The ability to estimate battery temperature, especially the internal temperature, is of paramount importance to the battery management system for monitoring and thermal control purposes. In this paper, a data-driven approach combining the RBF neural network (NN) and the extended Kalman filter (EKF) is proposed to estimate the internal temperature for lithium-ion battery thermal management. To be specific, the suitable input terms and the number of hidden nodes for the RBF NN are first optimized by a two-stage stepwise identification algorithm (TSIA). Then, the teaching-learning-based optimization (TLBO) algorithm is developed to optimize the centres and widths in every neuron of basis function. After optimizing the RBF NN model, a battery lumped thermal model is adopted as the state function with the EKF to filter out the outliers of the RBF model and reduce the estimation error. This data-driven approach is validated under four different conditions in comparison with the linear NN models. The experimental results demonstrate that the proposed RBF data-driven approach outperforms the other approaches and can be extended to other types of batteries for thermal monitoring and management.

Suggested Citation

  • Kailong Liu & Kang Li & Qiao Peng & Yuanjun Guo & Li Zhang, 2018. "Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management," Complexity, Hindawi, vol. 2018, pages 1-15, July.
    • Handle: RePEc:hin:complx:9642892
    DOI: 10.1155/2018/9642892
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    References listed on IDEAS

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    1. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
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    Cited by:

    1. Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. You, Yuqiang & Lin, Mingqiang & Meng, Jinhao & Wu, Ji & Wang, Wei, 2024. "Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation," Energy, Elsevier, vol. 304(C).
    3. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Khan, Ayesha & Naqvi, Ijaz Haider & Bhargava, Cherry & Lin, Chun-Pang & Boles, Steven Tyler & Kong, Lingxi & Pecht, Michael, 2025. "Safety and reliability analysis of lithium-ion batteries with real-time health monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    5. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    6. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    7. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).

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