A Comparative Analysis of Route-Based Energy Management Systems for Phevs

Abstract

Plug-in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero-emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A-ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine-tuned for the Toyota Prius plug-in hybrid powertrain and their performances are evaluated by using a high-fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A-ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A-ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.