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Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles

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Listed:
  • Yong Tian

    (Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, the University Town, Shenzhen 518055, China)

  • Bizhong Xia

    (Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, the University Town, Shenzhen 518055, China)

  • Mingwang Wang

    (Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, China)

  • Wei Sun

    (Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, China)

  • Zhihui Xu

    (Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, China)

Abstract

State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:12:p:8446-8464:d:43670
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    References listed on IDEAS

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    Cited by:

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    2. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    3. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    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.
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    6. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    7. Lin, Cheng & Tang, Aihua & Xing, Jilei, 2017. "Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 394-404.
    8. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    9. 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.
    10. Bizhong Xia & Shengkun Guo & Wei Wang & Yongzhi Lai & Huawen Wang & Mingwang Wang & Weiwei Zheng, 2018. "A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries," Energies, MDPI, vol. 11(10), pages 1-15, October.
    11. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    12. Xu Lei & Xi Zhao & Guiping Wang & Weiyu Liu, 2019. "A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power," Energies, MDPI, vol. 12(19), pages 1-24, September.
    13. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    14. Hong Zhang & Li Zhao & Yong Chen, 2015. "A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-18, December.
    15. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    16. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.

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