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State of Charge Estimation of Flooded Lead Acid Battery Using Adaptive Unscented Kalman Filter

Author

Listed:
  • Abdul Basit Khan

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea)

  • Abdul Shakoor Akram

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea)

  • Woojin Choi

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea)

Abstract

Flooded Lead Acid (FLA) batteries remain a cost-effective choice in various industries. Accurate State of Charge (SOC) estimation is crucial for effective battery management systems. This paper thoroughly examines the behavior of Open-Circuit Voltage (OCV) during hysteresis in FLA batteries, proposing a novel hysteresis modeling approach based on this behavior to enhance the SOC estimation accuracy. Additionally, we introduce an Adaptive Unscented Kalman Filter (AUKF) to further refine the SOC estimation precision. Experimental validation confirms the effectiveness of the proposed hysteresis modeling. A comparative analysis against the traditional Unscented Kalman Filter (UKF) under random charge/discharge profiles underscores the superior performance of AUKF, showcasing an improved convergence to the correct SOC value and a significant reduction in the SOC estimation error to approximately 2%, in contrast to the 5% error observed with the traditional UKF.

Suggested Citation

  • Abdul Basit Khan & Abdul Shakoor Akram & Woojin Choi, 2024. "State of Charge Estimation of Flooded Lead Acid Battery Using Adaptive Unscented Kalman Filter," Energies, MDPI, vol. 17(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1275-:d:1352764
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    References listed on IDEAS

    as
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    2. M. Armand & J.-M. Tarascon, 2008. "Building better batteries," Nature, Nature, vol. 451(7179), pages 652-657, February.
    3. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    4. 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.
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