Abstract:

In view of the poor rationality in selecting the working conditions for powertrain drivability evaluation， an optimum condition selection method is proposed for powertrain drivability evaluation based on combined weight and improved TOPSIS method. Firstly， the objective evaluation indicators of drivability are analyzed according to the working principle of powertrain with the software and hardware for testing and analysis developed. Then， based on the idea of combined weighting and adopting analytic hierarchy process （AHP） and coefficient of variation （CV） methods， the subjective and objective weights are determined， and the principle of minimum relative information entropy is applied to obtaining the optimum weights of AHP?CV model. Furthermore， the improved TOPSIS method with weighted Mahalanobis distance instead of Euclidean distance is used to build the optimum selection model for vehicle drivability evaluation conditions. Finally， the static gear shifting conditions are studied as an example， and the values of weighted indicators of 10 sub?conditions are obtained， with the sub?conditions comprehensively sorted. The test results show that the proposed improved AHP?CV?TOPSIS model has good suitability in condition selection for vehicle drivability evaluation， and can reveal the degree of importance of each sub?condition for drivability evaluation， providing scientific guidance in condition selection for subjective and objective evaluations of vehicle drivability.

Key words: drivability evaluation, optimum condition selection, test verification, improved AHP?CV?TOPSIS model