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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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    Cited by:

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    2. Bragadeshwaran Ashok & Chidambaram Kannan & Byron Mason & Sathiaseelan Denis Ashok & Vairavasundaram Indragandhi & Darsh Patel & Atharva Sanjay Wagh & Arnav Jain & Chellapan Kavitha, 2022. "Towards Safer and Smarter Design for Lithium-Ion-Battery-Powered Electric Vehicles: A Comprehensive Review on Control Strategy Architecture of Battery Management System," Energies, MDPI, vol. 15(12), pages 1-44, June.
    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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