Stochastic Real-World Drive Cycle Generation Based on a Two Stage Markov Chain Approach 2015-01-0488

This paper presents a methodology and tool that stochastically generates drive cycles based on measured data, with the purpose of testing and benchmarking light duty vehicles in a simulation environment or on a test-bench. The WLTP database, containing real world driving measurements, was used as input data. Consequently cycles that contain typical accelerations per velocity and road types are generated, such that these cycles are representative to real driving behavior.

The stochastic drive cycle generator is developed in Matlab and is based on Markov processes. Two separate stochastic generators are used: one for generating the road type and one for generating the vehicle acceleration. First, a random road type profile is generated from the four different road types that are considered in the WLTP database: urban, rural, motorway and high-motorway, each of them with sub-road types based on different velocity bins. For each sub-road type, speed data and acceleration data will be used to partition the data into classes and states. The second random generator function is used to generate the vehicle acceleration corresponding to the already generated road type. Then, the speed profile is derived from the random generated acceleration profile.