Study On Energy Management Strategy For Plug-in Hybrid Electrical Vehicle

Plug-in hybrid electric vehicle(PHEV) has a longer electric range, and improves the vehicle economy and reduces emission on the basis of ensuring driving performance, compared with general hybrid electric vehicle, for it has two kinds of energy sources--the rechargeable battery and engine, which attracts the national attention. It is the emphasis of PHEV to match the powertrain parameters reasonably and distribute the energy flow in different modes according to driving cycles and state of components, to improve vehicle fuel efficiency. Meanwhile, battery is a key for PHEV. Battery life will affect the performance and cost of a vehicle seriously. Thus the impact of battery life on energy management was taken into account and control parameters of energy management was optimized, which is important for ensuring performance and reducing cost. In this paper, the specific content included the following parts:The operation modes and drive modes were analyzed according to vehicle characteristics. Power requirement was calculated according to the basic parameters and performance targets of PHEV, based on comprehensive analysis of domestic and foreign researches on PHEV energy management. Then parameters matching for key components of the powertrain was carried out. Drive mode and brake mode control strategies were developed, and the control parameters were determined, based on analyzing the performance of key components, according to the design principles of PHEV, to maximize the efficiency of vehicle. Numerical models for key components of powertrain and dynamic model were built on MATLAB / Simulink platform, based on vehicle dynamics. Dynamic performance and rationality of the developed control strategies were verified.Reasons for attenuation of battery life and factors affecting battery life were analyzed. The important indicator named battery life was added into optimization targets of PHEV energy management, based on existing battery life model. Control parameters of energy management were optimized with genetic algorithm. Vehicle fuel consumption was reduced 1.1% and battery capacity fading was decreased 3% after optimization.In order to solve the problem that the control strategy depend on driving cycles, standard driving cycles were divided into three categories: urban congestion cycles, highway cycles, and urban suburban cycles. Characteristic parameters of each driving pattern were calculated, and the driving patterns were recognized with probabilistic neural network. Simulation shows satisfactory accuracy of the recognized method. Then a comprehensive test driving cycle was constructed, composed of basic driving cycles. Driving cycle recognized strategy was developed with probabilistic neural network. Simulation results shows battery capacity fading was increased 2%, vehicle fuel consumption was reduced 11%, comprehensive fuel consumption was reduced 6%, compared with the rule-based control strategy.