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Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Author

Listed:
  • Sara Rahimifard

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Saeid Habibi

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Gillian Goward

    (Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Jimi Tjong

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

Abstract

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2 % over the full operating range of SoC along with an accurate estimation of SoH.

Suggested Citation

  • Sara Rahimifard & Saeid Habibi & Gillian Goward & Jimi Tjong, 2021. "Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries," Energies, MDPI, vol. 14(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8560-:d:705912
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    References listed on IDEAS

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    1. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(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.
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    Cited by:

    1. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost," Energies, MDPI, vol. 15(16), pages 1-18, August.

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