In order to improve the fuel economy and to satisfy the customer acceptance constraints, Hybrid Electric Vehicles (HEVs) were introduced in the market. By integrating an internal combustion engine and an electrical system (one or more electric motor/generators (MGs)(MGs and a battery) in the powertrain, HEVs can combine the benefits of electrical vehicles and conventional internal combustion engine vehicles.
In this research project, the problem of optimizing the design of parallel P2 full and plug-in HEVs powertrain was addressed considering as test vehicle the Fiat Ducato delivery van. The exploration of the design space is done using a brute force, by varying only two design parameters and fixing the others (the 74 design parameters are: engine power scale, hybridization factor, battery capacity and gear ratios), and the best candidates, which are the best design solutions for our hybrid delivery van, are identified based on multiple performance objectives: the fuel consumption, battery energy consumption, CO2 emissions, total cost and 0-100km/h acceleration time test.
We used as a control algorithm to accelerate the prediction of the fuel economy the near-optimal, off-line control algorithm known as SERCA algorithm starting from a vehicle model implemented in MATLAB software and various predefined drive missions. Then the particle swarm optimization algorithm was employed to calculate the best CO2 emission minimization and best total cost.