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Aiming at the problem that the evaluation model had proposed by researchers to evaluate the drivability of a vehicle in the process of engine start to exist poor stability and poor accuracy. In this paper, a drivability evaluation model combined with principal component analysis and support vector regression is proposed. In this evaluation model, the principal component analysis is adapted to determine the input index of evaluation model, and the drivability evaluation model is built on the basis of support vector regression. The experimental results demonstrate that the drivability evaluation model is proposed by this paper has higher accuracy and stability than the model using the BP neural network. This method can be as well extended to other evaluation models, with higher theoretical guidance and application value in practical issues.

The application of energy efficient technologies reduces the driving qualities of vehicles. Taking subjective evaluation methods to evaluate vehicle qualities is costly, poor reproducibility, seriously affecting the vehicle calibration cycle. In order to overcome the drawbacks of the subjective evaluation methods, this paper analyzes the characteristics of the content of drivability evaluation, and the subjective evaluation system is modified for an objective evaluation system. A Fuzzy Hierarchy quantization method is proposed based on AHP (Analysis Hierarchy Progress) and Fuzzy comprehensive method to quantify the content of drivability evaluation.