Accurate and stable estimation of vehicle driving state information is essential for vehicle kinematic control and active safety control. The thesis takes distributed-driven electric vehicles as the research object, and designs an adaptive vehicle driving state estimation method based on the federal-cubature Kalman filter theory. The corresponding nonlinear three-degree-of-freedom vehicle dynamics model is established and the state space equations are obtained. By using information fusion technology to fuse low-cost sensor signals with multiple information sources, and using vehicle dynamics theory to build a vehicle driving state estimator. Select typical experimental conditions and apply Simulink to build the algorithm model and co-simulated with CarSim for verification. Experimental results show that the proposed estimation method can improve accuracy and stability of state estimation.


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    Title :

    Driving State Estimation of Distributed Drive Electric Vehicle Based on Federal-Cubature Kalman Filter


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Yanan (author) / Li, Gang (author) / Fan, Dongsheng (author) / Lou, Sufeng (author)


    Publication date :

    2022-01-13


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English