Research On PHEV Predictive Energy Management Strategy Based On Historical Data And Traffic Information

China is facing with energy shortage,serious environmental pollution and other problems,Plug-in Hybrid Electric Vehicle(PHEV),as an effective way to achieve energy saving and emission reduction under current technical conditions,has been the focus of the government and enterprises.The energy management strategy of PHEV directly affects the energy saving and emission reduction capability of PHEV.The development of intelligent transportation technology provides new ideas for energy management strategy formulation.It can provide reference for the real-time optimization of energy management strategy for the long-term prediction of future operating conditions by integrating the information of intelligent transportation.In order to better allocate PHEV power and further improve PHEV fuel economy,this paper takes P2.5 PHEV as the research object,and carries out research on PHEV predictive energy management strategy based on historical data and traffic information.The main research contents are as follows:(1)The P2.5 configuration PHEV system structure was analyzed,the power flow in each working mode was described in detail,and the numerical model of the key components was established based on the bench test data.Energy management strategy based on rules is established to determine the operation mode of the two kinds of operation mode,set the mode switch conditions and torque distribution rules,repeated NEDC condition is given to demonstrate the numerical model is set up by the validity and the rationality of the energy management strategy based on rules,provides reference for subsequent energy management strategy formulation.(2)The energy management strategy of PHEV based on Dynamic Programming(DP)algorithm was established,the solution method of DP was studied,and the optimal energy allocation was realized.The State of Charge(SOC)of the battery under the condition of repeated NEDC was studied under the global optimal energy allocation,which laid a theoretical foundation for the establishment of the reference trajectory of the theoretical SOC.Compared with the rule-based control strategy,the fuel economy of PHEV based on DP strategy is greatly improved,which verifies the effectiveness of the proposed PHEV energy management strategy based on DP algorithm,and also provides a reference for the energy management strategy in the following paper.(3)A PHEV energy management strategy based on Model Predictive Control(MPC)was constructed,and DP and MPC were combined to optimize the solution.A method to reduce the computational load of dynamic programming is studied.Two kinds of prediction models are used to predict the future speed,and the prediction effects of the two models are compared.The energy management strategy based on MPC is simulated and analyzed.In the prediction time domain,based on the real speed,the necessity of the SOC reference trajectory is verified,and the effectiveness of the rolling optimization method is verified.The simulation results of MPC control strategy based on rule,DP and two prediction models are compared.The simulation results show that the fuel economy of the energy management strategy based on MPC is obviously better than that of the rule-based energy management strategy,and the fuel economy is close to that of the DP strategy,which verifies the effectiveness of the MPC strategy.At the same time,the results of MPC strategy under the two vehicle speed prediction models also show that improving the accuracy of vehicle speed prediction can improve fuel economy.The control effect of MPC strategy at real speed also indicates that the theoretical SOC reference trajectory has some limitations.(4)A PHEV predictive energy management strategy based on historical data and traffic information is proposed.For the need of PHEVs to expect SOC reduction to the minimum value at the end of the trip period,a method of SOC planning based on driver historical travel data and real-time traffic information is proposed to plan SOC from a global perspective,and the planned SOC trajectory is introduced into the MPC strategy proposed in Chapter 4 as a constraint within the short-term prediction interval for real-time solution.The method of how to forecast global operating conditions and how to plan SOC according to the forecast result of global operating conditions is described.In order to obtain historical traffic data,a small traffic environment of Vissim was reconstructed in Prescan,based on which historical traffic data were obtained from the constructed traffic environment by driving devices.Simulation verification was carried out,and the results of MPC strategy,rule-based strategy and DP-based strategy with or without traffic information were compared and analyzed.The simulation results show that the fuel economy of the MPC strategy with or without traffic information participation is obviously better than that of the rule-based strategy.With the participation of traffic information,the fuel economy of the MPC strategy reached 90.65% of the DP strategy,which was 1.33% higher than that of the MPC strategy based on the theoretical SOC trajectory.The effectiveness of the proposed predictive energy management strategy was verified.