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Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

Author

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  • Ming Cai

    (School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China)

  • Weijie Chen

    (School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China)

  • Xiaojun Tan

    (School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China)

Abstract

State-of-charge (SOC) estimation is essential for the safe and effective utilization of lithium-ion batteries. As the SOC cannot be directly measured by sensors, an accurate battery model and a corresponding estimation method is needed. Compared with electrochemical models, the equivalent circuit models are widely used due to their simplicity and feasibility. However, such integer order-based models are not sufficient to simulate the key behavior of the battery, and therefore, their accuracy is limited. In this paper, a new model with fractional order elements is presented. The fractional order values are adaptively updated over time. For battery SOC estimation, an unscented fractional Kalman filter (UFKF) is employed based on the proposed model. Furthermore, a dual estimation scheme is designed to estimate the variable orders simultaneously. The accuracy of the proposed model is verified under different dynamic profiles, and the experimental results indicate the stability and accuracy of the estimation method.

Suggested Citation

  • Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1577-:d:114767
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    2. Fang Liu & Jie Ma & Weixing Su & Hanning Chen & Maowei He, 2020. "Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation," Energies, MDPI, vol. 13(7), pages 1-19, April.
    3. Xu, Cheng & Zhang, E & Jiang, Kai & Wang, Kangli, 2022. "Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery," Applied Energy, Elsevier, vol. 327(C).
    4. Xian Wang & Zhengxiang Song & Kun Yang & Xuyang Yin & Yingsan Geng & Jianhua Wang, 2019. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries," Energies, MDPI, vol. 12(1), pages 1-22, January.

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