Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing

Abstract

Growing concerns about petroleum supplies and air pollution have spurred increased interest and research into hybrid electric vehicles (HEVs). While standard driving cycles are commonly used for the purpose of propulsion chain optimization, the issue of how representative they actually are is questioned. This paper proposes a novel methodology for modeling real-world vehicle missions by considering the stochastic characteristics of the driving pattern and the dependence among the variables of the mission, i.e., vehicle speed, acceleration, and road gradient. The modeling procedure is based on a Markov matrix formulation, and a simulation algorithm is implemented to generate an unlimited number of stochastic mission profiles. Two kinds of mission natures have been modeled and discussed so as to stress the mission impact on the energy consumption according to the propulsion chain sizing. The approach is then validated using the architecture of a series HEV powered by a diesel generator and batteries. The results on fuel consumption are presented, and the benefit of the proposed method over conventional approaches is argued.