Driving Condition Recognition and Optimisation-Based Energy Management Strategy for Power-Split Hybrid Electric Vehicles

Power-split hybrid electric vehicles (PSHEVs) have the advantages of low fuel consumption, low emissions, and no mileage limitations, and their performance is largely determined by the control strategy. The purpose of the study was to solve the problem of driving condition recognition and energy management strategy (EMS) of PSHEVs. For this purpose, the parametric description method for the driving cycle conditions, the driving condition recognition method based on learning vector quantisation (LVQ) neural network (NN), and the energy management optimisation strategy for hybrid power systems based on predictive information were studied. Energy management optimisation under certain conditions was carried out by using Pontryagin’s minimum principle. A test bench platform for hybrid power systems was built to verify the effectiveness of the energy management and control strategy for HEVs based on condition recognition. Computer simulation and experimental results show that the presented EMS can effectively control hybrid power systems and significantly improve fuel economy compared with other control strategies.