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A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries

Author

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  • Xintian Liu

    (Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China)

  • Xuhui Deng

    (Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China)

  • Yao He

    (Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China)

  • Xinxin Zheng

    (Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China)

  • Guojian Zeng

    (Anhui Ruineng Technology Company, Hefei 230011, China)

Abstract

With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.

Suggested Citation

  • Xintian Liu & Xuhui Deng & Yao He & Xinxin Zheng & Guojian Zeng, 2019. "A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:121-:d:301943
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    References listed on IDEAS

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    1. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    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. 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.
    4. 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.
    5. Fei Feng & Rengui Lu & Guo Wei & Chunbo Zhu, 2015. "Online Estimation of Model Parameters and State of Charge of LiFePO 4 Batteries Using a Novel Open-Circuit Voltage at Various Ambient Temperatures," Energies, MDPI, vol. 8(4), pages 1-27, April.
    6. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    7. Cheng Siong Chin & Zuchang Gao & Joel Hay King Chiew & Caizhi Zhang, 2018. "Nonlinear Temperature-Dependent State Model of Cylindrical LiFePO 4 Battery for Open-Circuit Voltage, Terminal Voltage and State-of-Charge Estimation with Extended Kalman Filter," Energies, MDPI, vol. 11(9), pages 1-28, September.
    8. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    9. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
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    Cited by:

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    3. Wojciech Cieslik & Filip Szwajca & Wojciech Golimowski & Andrew Berger, 2021. "Experimental Analysis of Residential Photovoltaic (PV) and Electric Vehicle (EV) Systems in Terms of Annual Energy Utilization," Energies, MDPI, vol. 14(4), pages 1-21, February.

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