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An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter

Author

Listed:
  • Zhibing Zeng

    (College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Jindong Tian

    (College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Dong Li

    (College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Yong Tian

    (College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.

Suggested Citation

  • Zhibing Zeng & Jindong Tian & Dong Li & Yong Tian, 2018. "An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:59-:d:124981
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    References listed on IDEAS

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    2. Jiang, Cong & Wang, Shunli & Wu, Bin & Fernandez, Carlos & Xiong, Xin & Coffie-Ken, James, 2021. "A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter," Energy, Elsevier, vol. 219(C).
    3. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
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    5. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    6. Haitao Zhang & Ming Zhou & Xudong Lan, 2019. "State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter," Energies, MDPI, vol. 12(20), pages 1-15, October.
    7. Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
    8. Damoon Soudbakhsh & Mehdi Gilaki & William Lynch & Peilin Zhang & Taeyoung Choi & Elham Sahraei, 2020. "Electrical Response of Mechanically Damaged Lithium-Ion Batteries," Energies, MDPI, vol. 13(17), pages 1-15, August.
    9. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
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