Energy, Economical and Environmental Analysis of Plug-In Hybrids Electric Vehicles Based on Common Driving Cycles 2009-24-0062

The objective draw by this project is to develop tools for Plug-in Hybrid Electric Vehicle (PHEV) design, energy analysis and energy management, with the aim of analyzing the effect of design, driving cycles, charging frequency and energy management on performance, fuel economy, range and battery life.

A Chevrolet Equinox fueled by bio diesel B20 has been hybridized at the Center for Automotive Research (CAR), at The Ohio State University. The vehicle model has been developed in Matlab/Simulink environment, and validated based on laboratory and test. The PHEV battery pack has been modeled starting from Li-Ion batteries experimental data and then implemented into the simulator.

In order to simulate “real world” scenarios, custom driving cycles/typical days were identified starting from average driving statistics and well-known cycles. The driving cycles are based on commonly accepted standardized cycles (FUDS, FHDS, etc) and then combined to reflect common driving habits, average commute time, thus resulting in an annual distance traveled of about 15.000 miles.

Several scenarios have been drawn considering different vehicle operation modes, i.e. EV (electric vehicle) and blended, different battery sizes, 6.97 and 8.87 kWh of stored energy and different charging availability, i.e. controlled (once a day, overnight) and uncontrolled (charging is possible whenever the vehicle is parked). For a complete assessment of the benefits achievable by PHEVs, results are compared to two other vehicle architectures (equivalent in terms of available power): the hybrid version of the proposed model and the conventional ICE vehicle (stock vehicle converted into B20). Results show significant benefits of PHEVs in terms of petroleum reduction, overall fuel cost and CO₂ emissions; it is also clear that none of the proposed configurations (i.e. different battery sizes) and vehicle operation modes (i.e. EV or blended) represents an absolute optimum, but the analysis strongly depends on the chosen objective function to minimize, vehicle components sizing and adapted strategies, fuel and electricity cost, charging availability and power grid generation mix.