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Health Monitoring of Lithium-Ion Batteries Using Dual Filters

Author

Listed:
  • Richard Bustos

    (College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Stephen Andrew Gadsden

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Pawel Malysz

    (Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Mohammad Al-Shabi

    (Department of Mechanical and Nuclear Engineering, University of Sharjah, Sharjah, United Arab Emirates)

  • Shohel Mahmud

    (College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada)

Abstract

Accurate estimation of a battery’s capacity is critical for determining its state of health (SOH) and retirement, as well as to ensure its reliable operation. In this paper, a dual filter architecture using the Kalman filter (KF) and the novel sliding innovation filter (SIF) was implemented to estimate the capacity and state of charge (SOC) of a lithium-ion battery. NASA’s Prognostic Center of Excellence (PCOE) B005 battery data set was selected for this experiment based on its wide use in academia and industry. This dataset contains cycling data of a 2 Ah lithium-ion battery until its capacity was measured at 1.3 Ah or less. The dual polarity equivalent circuit model (DP-ECM) was selected for modeling. The model parameter values were estimated using the least squares (LS) algorithm. Under normal operating conditions, both the dual-KF and dual-SIF performed similarly in terms of estimation accuracy. However, an uncertainty case was considered where the filters were subjected to rapid changing dynamics by cutting the data by 300 cycles. In this case, the battery capacity root-mean-square error (RMSE) for the dual-KF and the proposed dual-SIF were 0.1233 and 0.0675, respectively. Under rapidly changing dynamics and faulty conditions, the dual-SIF shows better convergence and robustness to disturbances.

Suggested Citation

  • Richard Bustos & Stephen Andrew Gadsden & Pawel Malysz & Mohammad Al-Shabi & Shohel Mahmud, 2022. "Health Monitoring of Lithium-Ion Batteries Using Dual Filters," Energies, MDPI, vol. 15(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2230-:d:774252
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    References listed on IDEAS

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    1. Ngoc-Tham Tran & Abdul Basit Khan & Thanh-Tung Nguyen & Dae-Wook Kim & Woojin Choi, 2018. "SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation," Energies, MDPI, vol. 11(7), pages 1-14, June.
    2. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    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. Hongwen He & Hongzhou Qin & Xiaokun Sun & Yuanpeng Shui, 2013. "Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms," Energies, MDPI, vol. 6(10), pages 1-13, September.
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    Cited by:

    1. Erwin Sutanto & Putu Eka Astawa & Fahmi Fahmi & Muhammad Imran Hamid & Muhammad Yazid & Wervyan Shalannanda & Muhammad Aziz, 2023. "Lithium-Ion Battery State-of-Charge Estimation from the Voltage Discharge Profile Using Gradient Vector and Support Vector Machine," Energies, MDPI, vol. 16(3), pages 1-20, January.

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