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Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

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  • Xiangwei Guo

    (New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
    College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China)

  • Longyun Kang

    (New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China)

  • Yuan Yao

    (New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China)

  • Zhizhen Huang

    (New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China)

  • Wenbiao Li

    (New Energy Research Center of Electric Power College, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China)

Abstract

An estimation of the power battery state of charge ( SOC ) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC . Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

Suggested Citation

  • Xiangwei Guo & Longyun Kang & Yuan Yao & Zhizhen Huang & Wenbiao Li, 2016. "Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm," Energies, MDPI, vol. 9(2), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:100-:d:63722
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    References listed on IDEAS

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    Cited by:

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    2. Natthawuth Somakettarin & Achara Pichetjamroen, 2019. "Characterization of a Practical-Based Ohmic Series Resistance Model under Life-Cycle Changes for a Lithium-Ion Battery," Energies, MDPI, vol. 12(20), pages 1-11, October.
    3. Bizhong Xia & Zizhou Lao & Ruifeng Zhang & Yong Tian & Guanghao Chen & Zhen Sun & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2017. "Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-23, December.
    4. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    5. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    6. Linghu, Jinqing & Kang, Longyun & Liu, Ming & Luo, Xuan & Feng, Yuanbin & Lu, Chusheng, 2019. "Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter," Energy, Elsevier, vol. 189(C).
    7. Yunfeng Jiang & Xin Zhao & Amir Valibeygi & Raymond A. De Callafon, 2016. "Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery," Energies, MDPI, vol. 9(8), pages 1-17, July.
    8. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    9. Zhongwei Deng & Lin Yang & Yishan Cai & Hao Deng, 2016. "Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries," Energies, MDPI, vol. 9(6), pages 1-16, June.
    10. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
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    12. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
    13. Xiaoya Shang & Zhigang Li & Tianyao Ji & P. Z. Wu & Qinghua Wu, 2017. "Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning," Energies, MDPI, vol. 10(11), pages 1-17, November.
    14. Xian Wang & Zhengxiang Song & Kun Yang & Xuyang Yin & Yingsan Geng & Jianhua Wang, 2019. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries," Energies, MDPI, vol. 12(1), pages 1-22, January.
    15. Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Fang Zhou & Cheng Chang & Da Wang, 2017. "Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering," Energies, MDPI, vol. 10(5), pages 1-13, May.
    16. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    17. Jiale Xie & Jiachen Ma & Jun Chen, 2018. "Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect," Energies, MDPI, vol. 11(5), pages 1-14, May.
    18. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    19. Xin Lu & Hui Li & Jun Xu & Siyuan Chen & Ning Chen, 2018. "Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model," Energies, MDPI, vol. 11(4), pages 1-18, March.
    20. Jinqing Linghu & Longyun Kang & Ming Liu & Bihua Hu & Zefeng Wang, 2019. "An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries," Energies, MDPI, vol. 12(7), pages 1-15, April.
    21. Zizhou Lao & Bizhong Xia & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares," Energies, MDPI, vol. 11(6), pages 1-15, May.
    22. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    23. Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
    24. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    25. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.

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