关键词: SOM神经网络, 马尔可夫模型, 汽车行驶工况, 小波分析

Abstract: The driving cycles of the car reflect the kinematic characteristics of the car driving on the road.Existing methods of constructing driving cycles often have the problems of poor granularity and low accuracy.In order to solve these problems in constructing of driving cycles,a fine-grained method for constructing vehicle driving cycles model is proposed,called Construction method of automobile driving cycles based on SOM and Markov model(ADCSM).First,the data is cleaned by Daubechies-4 wavelet.The cleaned data is divided into many short strokes.The 10 features of the short stroke are extracted.10 feature parameters are clustered by using SOM network,andclustered into the (1 * 3) neural network to obtain the clustering result sequence.Markov model is established through sequence.Finally constructing driving cycle is completed through the ADCSM algorithm.The obtained driving cycles are compared with the results of the traditional K-means clustering construction method.The experimental data show that the final error of ADCSM is 4.07%,while the traditional K-means Means error is 8.77%.ADCSM uses the SOM neural network clustering method to have higher clustering accuracy than the traditional K-means method,and has the ability to self-learn working conditions.ADCSM uses the Markov model method to reflect the conversion relationship of urban driving conditions.Compared with the traditional K-means driving conditions construction method,the granularity is finer,so the synthesized driving conditions are more effective than the traditional driving cycles and reflect the driving feature of the city.

Key words: Markov model, SOM neural network, Vehicle driving cycles, Wavelet analysis