| With the increasingly serious environmental problems and the emergence of energy crisis,people have begun to realize the importance of energy conservation and emission reduction.Pure electric vehicles have the advantages of zero fuel consumption and zero exhaust emissions,which is an inevitable trend of future development.However,due to a series of reasons such as high battery cost,short cruising range,and inconvenient charging,pure electric vehicles cannot be accepted by the vast majority of people,so they cannot replace mainstream traditional fuel vehicles.Fuel cell vehicles also have major safety hazards,and the technology cannot meet the requirements.Hybrid electric vehicles(HEV)have begun to occupy an increasingly important position in life.Hybrid vehicles can currently fill the gaps in pure electric vehicle technology,such as short range and slow charging.This paper takes the P2 configuration parallel battery-sustaining hybrid electric vehicle as the research object,and conducts a research on the energy management strategy of hybrid electric vehicle based on vehicle speed prediction.The model predictive control framework can be combined with different optimization algorithms to obtain the optimal solution in a short time,so it can be effectively applied in hybrid energy management.the main contents are as follows:(1)This paper uses the simulation software Matlab/Simulink to build the model of the whole vehicle and its various components(engine,motor,battery,transmission system),and adopts the method of backward modeling of the whole vehicle.(2)This paper adopts the commonly used hybrid energy management strategy of equivalent fuel consumption minimum strategy to obtain the optimal control by adjusting the equivalent factors corresponding to different working conditions,which is a reference for subsequent control.(3)When the current working condition information is unknown,a variety of algorithms(exponential function prediction and neural network model prediction)are used to predict the standard cycle working condition.The exponential prediction calculation is simple,but the accuracy is low.Neural network prediction is computationally complex but has high accuracy.The appropriate prediction method is selected as a result of making comparison.(4)In this paper,the dynamic programming algorithm is used to solve the global optimization problem,the variables are discretized,and the optimal control sequence is obtained by the reverse solution and forward optimization method,and the control strategy(the optimal motor-engine distribution ratio)is obtained.The model predictive control framework is combined with different optimization algorithms(dynamic programming algorithm,adaptive equivalent fuel consumption minimization strategy algorithm),and the rationality and feasibility of model predictive control in energy management strategies are verified through simulation analysis.Finally,the feasibility of the algorithm is verified by hardware-in-the-loop. |
