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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    Titel :

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


    Weitere Titelangaben:

    Mechan. Machine Science


    Beteiligte:
    Khang, Nguyen Van (Herausgeber:in) / Hoang, Nguyen Quang (Herausgeber:in) / Ceccarelli, Marco (Herausgeber:in) / Feng, Jihao (Autor:in) / Qin, Datong (Autor:in) / Wang, Kang (Autor:in) / Liu, Yonggang (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    15.12.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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