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State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation

Author

Listed:
  • Chuan-Xiang Yu

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Yan-Min Xie

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Zhao-Yu Sang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Shi-Ya Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Rui Huang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

Abstract

State-of-charge estimation and on-line model modification of lithium-ion batteries are more urgently required because of the great impact of the model accuracy on the algorithm performance. This study aims to propose an improved DUKF based on the state-parameter separation. Its characteristics include: (1) State-Of-Charge (SoC) is treated as the only state variable to eliminate the strong correlation between state and parameters. (2) Two filters are ranked to run the parameter modification only when the state estimation has converged. First, the double polarization (DP) model of battery is established, and the parameters of the model are identified at both the pulse discharge and long discharge recovery under Hybrid Pulse Power Characterization (HPPC) test. Second, the implementation of the proposed algorithm is described. Third, combined with the identification results, the study elaborates that it is unreliable to use the predicted voltage error of closed-loop algorithm as the criterion to measure the accuracy of the model, while the output voltage obtained by the open-loop model with dynamic parameters can reflect the real situation. Finally, comparative experiments are designed under HPPC and DST conditions. Results show that the proposed state-parameter separated IAUKF-UKF has higher SoC estimation accuracy and better stability than traditional DUKF.

Suggested Citation

  • Chuan-Xiang Yu & Yan-Min Xie & Zhao-Yu Sang & Shi-Ya Yang & Rui Huang, 2019. "State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation," Energies, MDPI, vol. 12(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4036-:d:279576
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    References listed on IDEAS

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    Cited by:

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    3. Gelareh Javid & Djaffar Ould Abdeslam & Michel Basset, 2021. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks," Energies, MDPI, vol. 14(3), pages 1-14, February.
    4. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    5. 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).
    6. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.

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