Research of Multi-objective Parameter Decoupled Optimization Method for Hybrid Electric Vehicle - Yiran Zhang; Han Zhao; Kang Huang; Mingming Qiu (Jixie chuandong)
The energy management strategy (EMS) and the powertrain parameter of hybrid electric vehicles are highly coupled. In the process of hybrid electric vehicle optimization, there is a continuous cycle, which makes it difficult to achieve parameter optimization and find the optimal solution. Aiming at this problem, a multi-parameter decouped optimization method is proposed, which adopts hybrid optimization strategy, taking the dynamic targets as constraint conditions and using particle swarm optimization algorithm to optimize powertrain parameter, the Particle swarm optimization (PSO) is used to optimize the energy management strategy and shifting strategy under different parameters. Aiming at a parallel hybrid vehicle, a forward model that includes a fuzzy PID driver is established by using Matlab/Simulink to self-adapt to the changing powertrain configurations. The results show that the hybrid optimization methodology is able to squeeze the potential of the vehicle, compared with the optimization method that simultaneously optimizes the logic gate threshold, the economy performance is enhanced by 4.55% and simultaneously, the energy management strategy and shifting strategy for the HEV under these parameters are obtained.