Research On Energy Management Strategy Of Series-parallel HEV Power System

The impact of traditional vehicles on energy and the environment is becoming more and more prominent,and the development of new energy vehicles is urgently needed for the automotive industry to achieve sustainable development.Hybrid Electric Vehicle(HEV)is a kind of new energy vehicle with significant energy saving and emission reduction.The energy management strategy of the Series-Parallel HEV power system is the core technology to achieve low fuel consumption for the whole vehicle.To explore the degree of development of different energy management strategies on the consumption reduction performance of Series-Parallel HEV,a Series-Parallel HEV is used as a research object,and the control objectives are to reduce the overall vehicle fuel consumption and actively maintain the power battery power balance,and to carry out double fuzzy control,Equivalent Consumption Minimization(ECMS)and dynamic planning for the Series-Parallel HEV.The three energy management strategies of double fuzzy control,equivalent consumption minimization strategy(ECMS),and dynamic programming(DP)algorithm are studied for Series-Parallel HEV.The main research contents are summarized as follows:(1)Energy management strategy design based on double fuzzy control.(1)Design of energy management strategy based on double fuzzy control.First,the energy flow direction of each operating mode of Series-Parallel HEV is analyzed and the energy flow direction model of the whole vehicle is established;then the mode switching controller of the whole vehicle is designed;finally,the double fuzzy controller is designed according to the difference of energy flow direction of series and parallel modes,and to make the double fuzzy control energy management strategy have better power maintenance,the power deviation is used as the input of the double fuzzy controller.stage engine output larger torque,the dual fuzzy controller is improved by using the engine torque adjustment signal as the output of the parallel fuzzy controller.(2)Design of ECMS-based transient optimal energy management strategy.Firstly,the equivalent fuel consumption minimum objective function of ECMS is constructed,the state variables and control variables of the system are determined,and the constraints of the system are established;then the equivalent fuel consumption model of Series-Parallel HEV is established,which contains two parts of research: the engine fuel consumption model and the power battery equivalent fuel consumption model,and the power battery output energy is equated to the engine fuel consumption by the equivalence factor in the power battery equivalent fuel consumption model;Finally,the golden partition method is used to solve the instantaneous optimal output power ratio of engine and power battery.(3)Design of global optimal energy management strategy based on the DP algorithm.Firstly,the cost function of the DP control strategy is constructed;then the state variables and control variables are discretized in stages,and the inverse calculation is carried out from the termination stage of the driving cycle to the initial stage,and the minimum cumulative state transfer cost function and the corresponding control variables are obtained from all stages by comparing the inverse calculation;finally,the forward optimization search is carried out from the initial stage of driving cycle to the termination stage by combining the initial state of the system,and the global optimal output power of the engine and Finally,the global optimal output power ratio of the engine and power battery is obtained by combining the initial state of the system from the initial phase to the termination phase of the driving cycle.The results show that the designed three strategies meet the power balance requirements under WLTC conditions,among which the ECMS control strategy has the best power maintenance with a power deviation of 0.1%;compared with the double fuzzy control strategy,the DP control strategy and the ECMS control strategy reduce fuel consumption by 10.73% and 6.72%,respectively.4.29%.