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

Abdul Basit Khan, Abdul Shakoor Akram and Woojin Choi ()
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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

Energies, 2024, vol. 17, issue 6, 1-15

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.

Keywords: hysteresis modeling; flooded battery; SOC estimation; Unscented Kalman Filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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