Gear-shift decision-making is the key to affect the power, economy and comfort of the vehicle. At present, gear-shift decision-making mostly relies on manual calibration, and it is impossible to make reasonable decisions based on real-time changes in driving intentions. The degree of intelligence needs to be improved. This paper proposes an online gear-shift decision-making method considering the driving intention. Based on the theoretical framework of model predictive control, in the predicting horizon, the multi-layer perception machine is used to predict the short-term vehicle states, and K-means clustering and deep belief network method are used to classify and recognize driving intentions. A gear-shift decision-making optimization model considering the fuel consumption and the driving intention is built in the predicting horizon, and the optimal gear-shift decision-making sequence is solved by the dynamic programming algorithm. The simulation and experiments show that the proposed online gear-shift decision-making method can improve the vehicle’s adaptability to driving behavior while saving fuel, so as to improve the intelligence of the gear-shift decision-making of the vehicle.


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    Title :

    Online Intelligent Gear-Shift Decision of Vehicle Considering Driving Intention Using Moving Horizon Strategy


    Additional title:

    Mechan. Machine Science


    Contributors:

    Conference:

    IFToMM Asian conference on Mechanism and Machine Science ; 2021 ; Hanoi, Vietnam December 15, 2021 - December 18, 2021



    Publication date :

    2021-12-15


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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