Energy Management Strategy Of Plug-in Hybrid Electric Vehicle Based On Trip Classification

Plug-in hybrid electric vehicle(Plug-in Hybrid Electric Vehicle,PHEV)not only has the characteristics of zero emission of pollution gas and low fuel consumption in the driving process,but also has the characteristics of long driving range and low manufacturing cost of the whole vehicle.It is the choice for the transition from traditional fuel vehicles to pure electric vehicles.Firstly,this paper builds MATLAB/Simulink vehicle simulation platform based on Chevrolet model.Then,by fully considering the distribution of users’ daily travel positions,the driving lines are reasonably classified,and an optimal control method of travel classification suitable for operation is proposed.Finally,in order to solve the optimal control problem of energy management strategy,this paper uses the adaptive Pontryagin minimum algorithm(APMP)to find the global optimal solution of the energy management problem.The algorithm is combined with the proposed stroke classification optimization control method to optimize fuel consumption.The research content of this paper is as follows:(1)Build MATLAB/ Simulink vehicle simulation platform.Model based on the Chevrolet Volt in the dynamic system,build a reasonable simulink vehicle dynamic model.For example,the set parameters in the model are matched with the real parameters of the key components of the power system in the real vehicle,and the vehicle experimental simulation platform is built according to the mathematical model of the key components inside the vehicle,so as to provide a reliable simulation platform for subsequent experiments.(2)A classification optimization control method for daily travel is proposed.Because the same user’s daily trips are regular and the number of common destinations is limited,a travel classification optimization control method based on the daily driving position is proposed by analyzing the distances of different charging positions.The method can obtain real-time road location information through GPS,and classify users’ trips according to classification principles and classification method strategies,so that the vehicles can stably switch between the optimal trip types under the condition of long-term regular charging,and run in pure electric mode to achieve the purpose of saving fuel consumption.(3)To verify the stability and reliability of the combination of adaptive Pontryagin minimum algorithm and daily trip classification optimization control method.In this paper,the proposed travel classification optimization control method is combined with A-PMP algorithm,and experimental simulation is carried out using FTP-72 urban road typical cycle conditions and Arco＿Merano actual cycle conditions.Four groups of simulation experiments were compared with typical optimization strategy(CD-CS)and optimization algorithm alone(A-PMP)in So C state maintenance and fuel consumption,respectively,when So C state was good and So C state reached the minimum set value during driving.According to four groups of experimental results,compared with pure optimization algorithm A-PMP and typical strategy CD-CS,both the method and the CD-CS method can achieve zero fuel consumption when the So C state is good.When the So C state reaches the minimum set value,Compared with c D-CS method and pure A-PMP optimization algorithm,the proposed method has obvious advantages in total fuel consumption,which verifies the stability and reliability of the proposed method in optimizing fuel consumption.