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An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

Author

Listed:
  • Thanh-Tung Nguyen

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

  • Abdul Basit Khan

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

  • Younghwi Ko

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

  • Woojin Choi

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

Abstract

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.

Suggested Citation

  • Thanh-Tung Nguyen & Abdul Basit Khan & Younghwi Ko & Woojin Choi, 2020. "An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter," Energies, MDPI, vol. 13(17), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4536-:d:407465
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    References listed on IDEAS

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    Cited by:

    1. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.

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