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A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model

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  • Bizhong Xia

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

  • Wenhui Zheng

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

  • Ruifeng Zhang

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

  • Zizhou Lao

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

  • Zhen Sun

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

Abstract

Accurate state of charge (SOC) estimation can prolong lithium-ion battery life and improve its performance in practice. This paper proposes a new method for SOC estimation. The second-order resistor-capacitor (2RC) equivalent circuit model (ECM) is applied to describe the dynamic behavior of lithium-ion battery on deriving state space equations. A novel method for SOC estimation is then presented. This method does not require any matrix calculation, so the computation cost can be very low, making it more suitable for hardware implementation. The Federal Urban Driving Schedule (FUDS), The New European Driving Cycle (NEDC), and the West Virginia Suburban Driving Schedule (WVUSUB) experiments are carried to evaluate the performance of the proposed method. Experimental results show that the SOC estimation error can converge to 3% error boundary within 30 seconds when the initial SOC estimation error is 20%, and the proposed method can maintain an estimation error less than 3% with 1% voltage noise and 5% current noise. Further, the proposed method has excellent robustness against parameter disturbance. Also, it has higher estimation accuracy than the extended Kalman filter (EKF), but with decreased hardware requirements and faster convergence rate.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1150-:d:107113
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    References listed on IDEAS

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    2. Bizhong Xia & Haiqing Wang & Mingwang Wang & Wei Sun & Zhihui Xu & Yongzhi Lai, 2015. "A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter," Energies, MDPI, vol. 8(12), pages 1-15, November.
    3. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.
    4. 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.
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    10. Yong Tian & Chaoren Chen & Bizhong Xia & Wei Sun & Zhihui Xu & Weiwei Zheng, 2014. "An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(9), pages 1-18, September.
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    Cited by:

    1. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    2. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
    3. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    4. Nicolae Tudoroiu & Mohammed Zaheeruddin & Roxana-Elena Tudoroiu, 2020. "Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-45, May.
    5. 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.
    6. Ruifeng Zhang & Bizhong Xia & Baohua Li & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation," Energies, MDPI, vol. 11(9), pages 1-20, August.
    7. Yun Zhang & Yunlong Shang & Naxin Cui & Chenghui Zhang, 2017. "Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 11(1), pages 1-15, December.
    8. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    9. Yao-Ching Hsieh & You-Chun Huang & Po-Chun Chuang, 2020. "A Charge-Equalization Circuit with an Intermediate Resonant Energy Tank," Energies, MDPI, vol. 13(24), pages 1-14, December.

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