Energy Management Strategy For PHEN Based On Of Equivalent Consumption Minimum Strategy With Condition Adaptation

The energy management strategy of plug-in hybrid electric vehicle(PHEV)is to optimize the torque distribution of each power source on the basis of ensuring the power performance,so as to achieve the goal of energy saving and emission reduction.At present,the precision of Beidou navigation satellite is constantly improving,and the big data and cloud computing industries are in the ascendant.The development direction of practical energy management strategy is:(1)to integrate the global travel information;(2)to realize the adaptive control of vehicle-human-infrastructure coordination;(3)to have high computing efficiency and reliable real-time application performance.Therefore,the main research tasks or contents of this paper are as follows:(1)the energy management strategy based on dynamic programming algorithm in the literature is studied to find the law or model that can be used for real-time control and can be extended when the control effect is optima;(2)Design of vehicle-human-infrastructure collaborative adaptive control method;(3)The REAL working conditions are collected and used as test conditions to study the control effect of the control strategy.The specific research work is as follows:Firstly,a simulation model suitable for the study of control strategy is established.It mainly includes:driver model,engine model,motor model,power battery model,vehicle longitudinal dynamics model and transmission system model;the regenerative braking energy recovery function is considered in the battery state transfer equation,which improves the simulation model and lays the foundation for the research of control strategy.Then,based on the analysis of the principle of real-time controllability of SOC trajectory based on equivalent consumption minimum strategy and the general regular pattern that the SOC decreases linearly with the mileage when the global driving distance is known,which can make the fuel economy close to the global optimum,by assuming that the SOC descent process of the battery is a process of maintaining the power balance instantaneously and the driving characteristics of the vehicle are stable in 150 seconds,the concept of condition identifier and equivalent factor predictor based on artificial neural network and equivalent consumption minimum strategy is proposed.After optimizing the relevant parameters.Taking the characteristic parameters of driving cycle as its input and the adaptive equivalent factor as its output,the adaptive control of vehicle human infrastructure coordination is realized.Regardless of the consumption of lighting,air conditioning and other electrical appliances and the impact of extreme weather on relevant parts of the vehicle.For CLTC-P×n condition,by comparing the fuel economy of energy management strategy based on the reference trajectory of SOC decreasing linearly with the mileage with that based on adaptive energy management strategy,the optimal equivalent factor dynamic adjustment parameters are obtained,and the optimization results is obtained,and the reason for optimization is analyzed,that is,the energy management strategy based on the reference trajectory of SOC decreasing linearly with the mileage,although it can keep the SOC trajectory close to the reference trajectory,however,the drastic fluctuation of the equivalent factor of the output results in the great change of the power distribution between the engine and the motor,which leads to the low efficiency of the engine.The simulation results show that the energy management strategy of fusion predictor achieves the goal of adaptive output of equivalent factor and improving the efficiency of energy use,and achieves the research task.Finally,the random condition(REAL condition)is collected as the test condition,and the optimal dynamic adjustment parameters of REAL 1 and REAL2 are obtained by studying the fuel economy of real condition based on adaptive energy management strategy.By comparing the optimal dynamic adjustment parameters of CLTC-P×n condition,REAL1 condition and REAL2 condition with the collection area of condition,the conclusion of accurate optimization of vehicle energy consumption based on driving route or region is obtained.