Driving Cycle Synthesis For Compressed Driving Information Based on Markov Chain

Paper #:
  • 2017-01-0424

  • 2017-03-28
Driving cycle is part of the fundamental data for the simulation and design of vehicle power train system. Most of the traditional cycles are developed for the test of emission or fuel consumption regulatory. It is inappropriate for the design processing in the vehicle development. What’ more, to avoid the cycle beating it requires the regulatory cycle has the robust performance. And for the popular driving cycles used in the researches, most of them do not take the slope information into account which has significant influence on the power train of the truck and electric vehicle. It is obvious that in the design process of the power train system, a robust and representative driving cycle is essential. In this paper, a stochastic model based driving cycle synthesis is discussed as a tool to generate the random driving cycle. The purpose of the tool is to compress the long driving data into a representative one as a prediction for the future driving task and help to implement the real-time configuration of the energy management system in the hybrid vehicles. The Markov Chain process is combined with the transition probabilities which are extracted from the input driving data to find out the next possible state of the vehicle. Specifically, the velocity and the slope are generated at the same time in the three-dimensional Markov Chain model. After the generation process, the result is verified by the selected criteria. Furthermore, it could work as a tool to generate the driving cycle with desired length to compress the original driving data. After the properties of the cycle should be the same as the original. The result needs to show that the compression of the driving cycle has a slight effect on the power train simulation. The simulation for a hybrid sedan model equipped with equivalence consumption management strategy is presented that the compressed cycle lead to the similar fuel-consumption.
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