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A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty

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  • Li, Yanwen
  • Wang, Chao
  • Gong, Jinfeng

Abstract

An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior.

Suggested Citation

  • Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2016. "A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty," Energy, Elsevier, vol. 109(C), pages 933-946.
    • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:933-946
    DOI: 10.1016/j.energy.2016.05.047
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    References listed on IDEAS

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    1. Lee, Seongjun & Kim, Jonghoon, 2015. "Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles," Energy, Elsevier, vol. 83(C), pages 462-473.
    2. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    3. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    4. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    5. Caiping Zhang & Jiuchun Jiang & Weige Zhang & Suleiman M. Sharkh, 2012. "Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering," Energies, MDPI, vol. 5(4), pages 1-18, April.
    6. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    7. Yong Tian & Chaoren Chen & Bizhong Xia & Wei Sun & Zhihui Xu & Weiwei Zheng, 2014. "An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(9), pages 1-18, September.
    8. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
    9. Zhiwei He & Mingyu Gao & Caisheng Wang & Leyi Wang & Yuanyuan Liu, 2013. "Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model," Energies, MDPI, vol. 6(8), pages 1-18, August.
    10. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    11. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    12. Hongwen He & Hongzhou Qin & Xiaokun Sun & Yuanpeng Shui, 2013. "Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms," Energies, MDPI, vol. 6(10), pages 1-13, September.
    13. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
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