ITS based Predictive Intelligent Battery Management System for plug-in Hybrid and Electric vehicles

Hybrid electric vehicles (HEV) have been developed to improve fuel efficiency and reduce emissions. The plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) have been recently widely analyzed for their additional significant potential of improving fuel efficiency and reducing emission. For PHEV, the charge depletion model of the high voltage (HV) battery has been identified to be the appropriate model to ensure that the vehicle reaches its destination while vehicle HV battery is at its minimum threshold, thus allowing minimal use of internal combustion engine (ICE). For EV, the charge depletion model is the natural phenomenon due to the vehicle architecture. However, the charge depletion model would not ensure optimization in fuel efficiency and reduced emissions due to its lack of knowledge of vehicle's relative traffic information. This drawback in the charge depletion model may be eliminated with the development of intelligent transportation system (ITS) which allows vehicles and road infrastructures to share historical, real time and predictive future traffic information. In this paper, the charge depletion model and a proposed intelligent predictive charge depletion model are compared. The overall performance, including trip time and fuel consumption of the models under a selection of drive cycles, will be demonstrated via simulation results.