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An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries

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  • Jinqing Linghu

    (New Energy Research Center, School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Longyun Kang

    (New Energy Research Center, School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Ming Liu

    (New Energy Research Center, School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Bihua Hu

    (New Energy Research Center, School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Zefeng Wang

    (New Energy Research Center, School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, School of Electric Power, South China University of Technology, Guangzhou 510640, China)

Abstract

Establishing a model equation with high accuracy and high computational efficiency is very important for the estimation of battery state of charge (SOC). To ensure better SOC estimation results, most studies have focused on the improvement of the algorithm, while the impact of the model equation which may offset the benefits of advanced algorithms has been overlooked. To address this problem, this paper studies the widely used model equations and presents a new model equation based on a Gaussian function that improves the SOC estimation accuracy and computational efficiency. With the Worldwide harmonized Light Vehicles Test Cycle (WLTC) which is highly dynamic and more realistic than any other driving cycles, the proposed model equation is applied to different filtering algorithms to validate its performance in SOC estimation. The results indicate that the proposed model equation can greatly improve the accuracy of SOC estimation without an increase of computation. In addition, for the traditional polynomial-based model equations, the 6th-order power function polynomial has better performance in SOC estimation than polynomials with other orders.

Suggested Citation

  • Jinqing Linghu & Longyun Kang & Ming Liu & Bihua Hu & Zefeng Wang, 2019. "An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries," Energies, MDPI, vol. 12(7), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1366-:d:221241
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    3. Linghu, Jinqing & Kang, Longyun & Liu, Ming & Luo, Xuan & Feng, Yuanbin & Lu, Chusheng, 2019. "Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter," Energy, Elsevier, vol. 189(C).

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