An integrated optimization approach for a hybrid energy system in electric vehicles

This paper develops a simple but innovative integrated optimization approach (IOA) for deriving the best solutions of component sizing and control strategies of a hybrid energy system (HES) which consists of a lithium battery and a supercapacitor module. To implement IOA, a multiple for-loop structure with a preset cost function is needed to globally calculate the best hybridization and energy management of the HES. For system hybridization, the optimal size ratio is evaluated by maximizing the HES energy stored capacity at various costs. For energy management, the optimal power distribution combined with a three-mode rule-based strategy is searched to minimize the total consumed energy. Combining above two for-loop structures and giving a time-dependent test scenario, the IOA is derived by minimizing the accumulated HES power. Simulation results show that 6% of the total HES energy can be saved in the IOA case compared with the original system in two driving cycles: ECE and UDDS, and two vehicle weights, respectively. It proves that the IOA effectively derives the maximum energy storage capacity and the minimum energy consumption of the HES at the same time. Experimental verification will be carried out in the near future.

► Second-order control-oriented dynamics for a battery/supercapacitor EV is modeled.
► Multiple for-loop programming and global searchwith constraints are main design principles of integrated optimization algorithm (IOA).
► Optimal hybridization is derived based on maximizing energy storage capacity.
► Optimal energy management in three EV operation modes is searched based on minimizing total consumed power.
► Simulation results prove that 6+% of total energy is saved by the IOA method.