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Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

Author

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  • Wenxian Duan
  • Chuanxue Song
  • Yuan Chen
  • Feng Xiao
  • Silun Peng
  • Yulong Shao
  • Shixin Song

Abstract

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

Suggested Citation

  • Wenxian Duan & Chuanxue Song & Yuan Chen & Feng Xiao & Silun Peng & Yulong Shao & Shixin Song, 2020. "Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-20, December.
  • Handle: RePEc:hin:jnlmpe:9502605
    DOI: 10.1155/2020/9502605
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    Cited by:

    1. Aihua Wu & Yan Zhou & Jingfeng Mao & Xudong Zhang & Junqiang Zheng, 2023. "An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(16), pages 1-24, August.
    2. Yongcun Fan & Haotian Shi & Shunli Wang & Carlos Fernandez & Wen Cao & Junhan Huang, 2021. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation," Energies, MDPI, vol. 14(8), pages 1-18, April.
    3. Ahmed Fathy & Dalia Yousri & Abdullah G. Alharbi & Mohammad Ali Abdelkareem, 2023. "A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters," Sustainability, MDPI, vol. 15(7), pages 1-22, March.

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