Integrated Optimization of Component Parameters and Energy Management Strategies for A Series-Parallel Hybrid Electric Vehicle

For the design of the hybrid electric vehicles, the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge. If handled sequentially, it is defined as sub-optimal. In order to obtain the optimal design of the system, it is necessary to integrate the physical system and its controller. Taking component parameters and energy management strategy as research objects, this paper elaborates an integrated optimization approach for a series-parallel hybrid electric vehicle. Firstly, a rule-based control strategy that can be applied online is designed according to various driving modes of the hybrid electric vehicle. Then, considering the coupling between component parameters and control strategies, a dual-layer optimization framework with genetic algorithm and double dynamic programming is proposed to optimize fuel economy and battery life. Among them, parameters of component size and control for the upper-layer of the framework are selected as preparative optimization parameters. In order to get rid of the influences of energy management strategies and obtain the optimal upper-layer parameters, the lower-layer of the framework adopts the global optimization algorithm to calculate the optimal energy distribution ratio for each driving mode. The results indicate that, while ensuring the good working condition of the battery, the fuel economy has improved by 7.79% under the selected driving cycle after optimization. The optimized upper-layer parameters combined with the proposed control rules can be applied online.