Stochastic MPC for Optimal Energy Management Strategy of Hybrid Vehicle performing ACC with Stop&Go maneuvers

Abstract

In urban zones, vehicles are often subject to multiple starts and stops, due mainly to different traffic conditions which are highly energy consuming. This paper investigates the problem of optimal energy management in a Plug-in Hybrid Electric Bus (PHEB) in urban environment for the purpose of energy consumption minimization. Hereby an original control architecture is proposed which comprises an Adaptive Cruise Control with Stop&Go (ACCwSG) control system and stochastic energy management strategy. To this end, first, an ACCwSG maneuvers has been developed with the aim of maintaining an inter-vehicular distance that ensures safety, as well as providing passenger comfort by generating smooth velocity profiles. Secondly, a Stochastic Model Predictive Control (SMPC) has been developed to optimize PHEB power split. Power demand is addressed as a Markov Decision Process (MDP). To prove the efficiency of the proposed strategy, it is compared with a deterministic rule based method. The obtained results demonstrate the reduction of the energy consumption in average around 13%. The present work is conducted on a dedicated high-fidelity dynamical model of the hybrid bus that was developed on MATLAB/TruckMaker software.