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In this study, a novel method for the construction of a driving cycle basedon Mean Shift clustering is proposed to solve the problems existing in thetraditional micro-trips method. Firstly, 1701 kinematic segments are obtainedby processing and dividing the driving data in real road conditions. Secondly,12 kinematic parameters are calculated for each segment, and the dimensionalityof parameters is reduced through principal component analysis (PCA). Threeprincipal components are chosen to classify all cycles into three types by theMean Shift algorithm. Finally, according to the principle of minimum deviation,representative micro-trips are selected from each type of cycle to complete theconstruction of the final driving cycle. Further, the construction method inthis paper is compared with the micro-trips construction method by the K-Meansclustering. The results show that the construction method by Mean Shiftclustering can more effectively reflect the real driving data. This studyrealizes the innovation in the construction method of micro-trips and providesa preliminary theoretical basis for the formulation of automobile workingcondition standards, energy management of new-energy vehicles, and optimalcontrol of vehicle dynamics in driverless vehicles.

Research on the construction method of vehicle driving cycle based on Mean Shift clustering