For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results.

Four-wheel-drive electric vehicles with multiple motors have several advantages such as energy saving, environmental protection, and excellent dynamics performance. Therefore, they have become an important development trend in the field of electric vehicles in the past few years. To deal with the problems of the economy and dynamics optimization of an electric vehicle with dual motors on the front axle and a single motor on the rear axle, a multi-mode drive optimization control strategy based on the hierarchical control architecture is proposed in this paper. In the strategy, the reference torque distribution strategy of the front and rear motors is obtained by optimizing the instantaneous energy consumption power in the economy module, and a torque compensation strategy is formulated based on the fuzzy control in the dynamics module to improve the vehicle dynamics in high dynamics demand conditions. Finally, the closed-loop cycle test and the driver-in-the-loop test are carried out to verify the performance of the strategy proposed in this paper, and the results show that energy consumption is reduced by 6.45% in the World Harmonized Light-duty Vehicles Test Cycle (WLTC), and that vehicles accelerate more quickly in variable acceleration conditions under the proposed strategy.