Application of SOM Neural Network in the Construction of Urban Ramp Driving Cycle

Abstract

In order to construct vehicle driving cycle with ramp characteristics, this paper applies SOM neural network to the construction of urban ramp driving cycle. Firstly, the data collected by the actual vehicle test is divided into micro-trips, after that the principal component analysis method is used to reduce the dimension of the selected 20 characteristic parameters, afterward the first five principal components of all the micro-trips are clustered by the SOM neural network, and then the micro-trips with the appropriate length of time are selected from each category to build a representative driving cycle with the smallest average relative error and stable slope angle-time curve. The research results show that the SOM neural network has high clustering accuracy in the construction of the ramp driving cycle.