A driving cycle construction methodology combining Markov chain with variation parameters and Monte Carlo

Abstract

When comparing the environmental protection and economy of different cars, it is necessary for cars to run in the same driving cycle to obtain the pollutant emission and fuel consumption. However, in the actual driving process, the performance of the vehicle may be markedly different from the performance in test cycle. In order to generate the driving cycle that can represent the actual driving process, this paper adopts the driving data of an express truck with specific driving routes to construct the typical driving cycle of a city by combining Markov chain with Monte Carlo random sampling. The random response is added in the construction process, and the variation parameter is used to simulate the sudden traffic situation. CCPV and CPV parameters are set to evaluate the generated driving cycle. Through Simulink simulation, the reliability of the generated driving cycle is verified and the influence of different statistical characteristics is determined.