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State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter

Author

Listed:
  • Ruolan Fan

    (Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China)

  • Gang Li

    (Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China)

  • Yanan Wu

    (Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China)

Abstract

As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provides more favorable conditions for vehicle active safety control. Acquiring accurate and real-time parameters such as vehicle speed and side slip angle is a prerequisite for vehicle active safety control. Therefore, relying on the National Natural Science Foundation of China, this paper takes the distributed drive electric vehicle in the form of four-wheel independent drive and steering as the research object. Taking the measurement data of low-cost vehicle sensors as input and adaptive Kalman filtering as theoretical support, the sub-filter of federal Kalman filtering adds a fuzzy controller on the basis of volumetric Kalman filtering, and designs the vehicle driving state estimation algorithm to realize the accurate estimation of driving state information. Finally, the typical experimental conditions are selected, and the designed algorithm is verified by the co-simulation of MATLAB/Simulink and CarSim. At the same time, the algorithm is further verified based on the driving simulator hardware-in-the-loop experimental platform. The results show that the designed estimation algorithm has good effects in terms of accuracy, stability, and real-time performance.

Suggested Citation

  • Ruolan Fan & Gang Li & Yanan Wu, 2023. "State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13446-:d:1235358
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    References listed on IDEAS

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    5. Hannes Heidfeld & Martin Schünemann, 2021. "Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle," Energies, MDPI, vol. 14(5), pages 1-23, March.
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