Abstract
In the actual energy and pollution context caused by road transport, city centers will be restricted to low emission vehicles i.e. hybrid vehicle and electric vehicle (HEV/EV). Moreover, the HEV/EV offer the opportunity to have various actuation architectures but raise the problem of over actuated systems. Therefore, it is of first interest to ask if energy gains can be obtained by managing the vehicle dynamic behavior in this urban situation. In previous works, studies about optimal architectures have been proposed and the objective of this paper is to investigate the effects of electric component dynamics on the battery and vehicle energy efficiencies. A simulation method is used in order to keep constant as much as possible of the parameters governing the vehicle energy efficiency. A control allocation is chosen to generate control signals to actuators for two selected vehicle architectures during a representative manoeuver. This control strategy allows to determine actuator inputs for architectures without changing control structure. These inputs are applied on actuators in a virtual test bench whose parameters have been validated from an actual delivery vehicle. This simulation model is supplemented or not by dynamic models of electrical components for all architectures. The electric component parameters are only considered to analyze the battery and vehicle consumed powers/energies in regenerative vehicle dynamics proposed in previous author works. Energy losses in tire/road contact or actuation energy in steering system are included in the energy comparison for the investigated architectures. The performance of the proposed control allocation is evaluated by comparison simulated trajectories of two selected vehicle architectures and reference trajectory with or without electric component models. The proposed regenerative vehicle dynamics is studied by analyzing the wheel torques and steering wheel angles. The simulation results of battery and vehicle consumed powers/energies are estimated and allow to determine the optimal vehicle architecture in terms of energy and the best electric component parameters. These results show that the proposed control allocation is robust with or without the integration of electric component models into vehicle dynamics. It also proves the effectiveness of the proposed method which is used to minimize energy consumption of hybrid vehicle. Moreover, the best values of electric component parameters in terms of consumed power/energy will be determined.
KEYWORDS – hybrid vehicle, energy efficiency, vehicle dynamics, optimal control, energy management, control allocation