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State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

Author

Listed:
  • Wenhui Zheng

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Bizhong Xia

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Wei Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

  • Yongzhi Lai

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

  • Mingwang Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

  • Huawen Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

Abstract

State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

Suggested Citation

  • Wenhui Zheng & Bizhong Xia & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer," Energies, MDPI, vol. 12(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2491-:d:243688
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    References listed on IDEAS

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    1. Shichun Yang & Cheng Deng & Yulong Zhang & Yongling He, 2017. "State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model," Energies, MDPI, vol. 10(10), pages 1-14, October.
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    Citations

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    Cited by:

    1. Li, Alan G. & Wang, Weizhong & West, Alan C. & Preindl, Matthias, 2022. "Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning," Applied Energy, Elsevier, vol. 315(C).
    2. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    3. Areeb Khalid & Syed Abdul Rahman Kashif & Noor Ul Ain & Muhammad Awais & Majid Ali Smieee & Jorge El Mariachet Carreño & Juan C. Vasquez & Josep M. Guerrero & Baseem Khan, 2023. "Comparison of Kalman Filters for State Estimation Based on Computational Complexity of Li-Ion Cells," Energies, MDPI, vol. 16(6), pages 1-20, March.
    4. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    5. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    6. Yue Ren & Chunhua Jin & Shu Fang & Li Yang & Zixuan Wu & Ziyang Wang & Rui Peng & Kaiye Gao, 2023. "A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries," Energies, MDPI, vol. 16(17), pages 1-38, August.

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