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Slope Estimation Method of Electric Vehicles Based on Improved Sage–Husa Adaptive Kalman Filter

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  • Jiawei Guo

    (School of Machinery and Transportation, Southwest Forestry University, Kunming 650224, China
    Key Laboratory of Vehicle Emission and Safety on Plateau Mountain, Yunnan Provincial Department of Education, Kunming 650224, China)

  • Chao He

    (School of Machinery and Transportation, Southwest Forestry University, Kunming 650224, China
    Key Laboratory of Vehicle Emission and Safety on Plateau Mountain, Yunnan Provincial Department of Education, Kunming 650224, China)

  • Jiaqiang Li

    (School of Machinery and Transportation, Southwest Forestry University, Kunming 650224, China
    Key Laboratory of Vehicle Emission and Safety on Plateau Mountain, Yunnan Provincial Department of Education, Kunming 650224, China)

  • Heng Wei

    (School of Machinery and Transportation, Southwest Forestry University, Kunming 650224, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China)

Abstract

In order to deal with many influence factors of electric vehicles in driving under complex conditions, this paper establishes the system state equation based on the longitudinal dynamics equation of vehicle. Combined with the improved Sage–Husa adaptive Kalman filter algorithm, the road slope estimation model is established. After the driving speed and rough slope observation are input into the slope estimation model, the accurate road slope estimation at the current time can be obtained. The road slope estimation method is compared with the original Sage–Husa adaptive Kalman filter road slope estimation method through three groups of road tests in different slope ranges, and the accuracy and stability advantages of the proposed algorithm in road conditions with large slopes are verified.

Suggested Citation

  • Jiawei Guo & Chao He & Jiaqiang Li & Heng Wei, 2022. "Slope Estimation Method of Electric Vehicles Based on Improved Sage–Husa Adaptive Kalman Filter," Energies, MDPI, vol. 15(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4126-:d:831301
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    References listed on IDEAS

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    1. Ke Song & Yimin Wang & Xiao Hu & Jing Cao, 2020. "Online Prediction of Vehicular Fuel Cell Residual Lifetime Based on Adaptive Extended Kalman Filter," Energies, MDPI, vol. 13(23), pages 1-21, November.
    2. Nan Lin & Changfu Zong & Shuming Shi, 2018. "The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics," Energies, MDPI, vol. 12(1), pages 1-15, December.
    3. Fan Zhang & Lele Yin & Jianqiang Kang, 2021. "Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms," Energies, MDPI, vol. 14(19), pages 1-18, October.
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