Discrete Grid Optimization of a Rule-Based Energy Management Strategy for a Formula Hybrid Electric Vehicle

Fuel economy and energy consumption in hybrid electric powertrain vehicles are highly dependent on managing power flow requirements. This opportunity has been minimally addressed in previous vehicles entered in the Formula Hybrid SAE competition. This paper outlines a method for determining an optimal rule-based energy management strategy for a post-transmission parallel hybrid electric vehicle developed at the University of Idaho. A supervisory controller determines the proper power split ratio between the available power sources (electrical and thermal). A GT-Suite model was used to simulate powertrain performance based on inputs of a numerically predicted engine performance map, an electric motor characteristic curve, vehicle data, road load parameters derived from a roll-down test, and vehicle driving cycle. The controller parameters included a switching speed below which the vehicle operated in electric mode (unless the battery state of charge was too low) and a power split ratio above the switching speed (provided the battery state of charge was adequate). The Artemis driving cycle which has an average speed of 30 mph and resembles the endurance course in the SAE Formula Hybrid competition, was the basis for the optimization study. Convergence was achieved in 272 iterations and resulted in a switching speed of 17.8 mph and a power split ratio above the switching speed of 24.5% electric energy and 75.5% thermal energy. This resulted in a 15% increase in fuel economy compared to a 50:50 energy split at all vehicle speeds. The optimization method is easily adaptable to other powertrain configurations and driving cycles, including the use of multiple switching speeds that would dynamically adjust the power split ratio.