Research And Simulation On Energy Management Strategy Of Hybrid Electric Vehicle

With the development of the global industry,people's demand for energy utilization and environmental protection is becoming higher and higher.Traditional vehicles have been unable to meet people's needs in terms of fuel saving and emissions,and new energy vehicles have emerged.Plug-in hybrid vehicles have been widely concerned by the community because of low fuel consumption,low emissions and long endurance mileage.With the constant vehicle hardware and driving conditions,fuel consumption mainly depends on energy management strategy,so this paper takes plugin series hybrid electric vehicle as the research object and researchs three energy management strategies.Firstly,the backward simulation model and forward simulation model of series hybrid electric vehicle are established,which are used to simulate off-line global optimization algorithm and on-line real-time control algorithm respectively.Secondly,the working mode of hybrid electric vehicle is analyzed,and the corresponding mode switching rules are set up,and the rule-based energy management strategy is simulated.Thirdly,the basic theory of dynamic programming algorithm is introduced and the energy management problem based on dynamic programming algorithm is constructed.The discrete SOC dynamic programming is improved by adding the rule that the vehicle is in the state of regenerative braking under braking condition and the calculation time is reduced.Based on the analysis of discrete SOC dynamic programming results,the calculation tendency of the algorithm is summarized and the results of the dynamic programming algorithm are modified to improve the accuracy of the algorithm.From this,a real-time energy management strategy based on rule extraction from dynamic programming is proposed.Finally,the energy management strategy based on model predictive control is constructed and the advantages and disadvantages of Markov stochastic prediction model and exponential function prediction model are analyzed and compared in power demand forecasting.A discrete power exponential function predicting method is proposed.By simulating the energy management strategy with discrete SOC and discrete engine output power dynamic programming as rolling optimization algorithms,it is concluded that the latter can overcome the inadequate accuracy of discrete SOC dynamic programming and realize real-time control and constraints on SOC.The fuel-saving effect is close to the global optimum.Based on the project "Development of Energy Management and Power Battery Group Detection System for Hybrid Electric Vehicles" of Hebei Science and Technology Program,this paper choosed three representative energy management strategies to simulate and concluded that the energy management strategy based on model predictive control with discrete engine output power dynamic programming as rolling optimization algorithm can achieve real-time optimal control,which has certain theoretical significance and practical value for the research of energy management strategy of hybrid electric vehicles.