Research On Design And Optimization Of Energy Management Strategy For Plug-in Hybrid Electric Vehicle

Hybrid electric vehicles（HEVs）share the characteristics of both battery electric vehicles（BEVs）and conventional vehicles,among which plug-in hybrid electric vehicles（PHEVs）are considered a solution for reducing fuel consumption and CO₂ emission of vehicles nowadays.Due to multi-sources of power in HEVs,fuel economy and emission heavily depend on the design of energy management strategy.Thus it is essential to study the energy management strategy of HEVs.This thesis investigates the design and optimization of energy management strategy for a PHEV.Firstly,simulation model of the investigated PHEV is built and verified.Based on main parameters of the vehicle,relevant sub-system mathematical models including ICE,electric motor and transmission are built.Then,simulation models are implemented in AVL Cruise accordingly.Apart from signal connection,models of different sub-systems are connected mechanically and electrically to form a complete PHEV model.And communicating signals between the PHEV model and energy management strategy are configured in MATLAB/Simulink.Then the PHEV simulation model is validated by worldwide light-duty test cycle（WLTC）in the co-simulating environment of MATLAB/Simulink and AVL Cruise.Secondly,an equivalent fuel consumption minimization strategy（ECMS）is designed and validated by co-simulation for the researched PHEV.First of all,the ECMS is implemented in MATLAB/Simulink based on minimum equivalent fuel consumption.Co-simulations for new European driving cycle（NEDC）show the effectiveness of the designed ECMS in this study.Furthermore,influence of fuel equivalent factor on fuel economy is studied during charge sustaining period of battery.Thirdly,optimization approach for the ECMS is investigated.As standard genetic algorithm（SGA）has the drawbacks of low convergence rate,early-maturing and population degradation,an improved GA approach is proposed by making probability of crossover and mutation adaptable along with the fitness value and iterative generation.Besides,elitist preservation operators are employed to protect good individuals from being knocked out during evolution process and to solve degradation problem of SGA.The improved genetic algorithm（IGA）is validated by Ackley test function.For improving fuel economy of the PHEV and meanwhile maintaining the charge of dynamic battery,the proposed IGA is used to optimize ECMS and corresponding co-simulations are carried out in MATLAB/Simulink and AVL Cruise co-simulation environment.The results show the effectiveness of the ECMS optimized by the IGA for the PHEV.Finally,hardware-in-the-loop（HIL）platform is built and HIL tests are conducted correspondingly.Based on ETAS/LabCar,the developed HIL platform is adopted to measure the performance of fuel economy of the ECMS,which is integrated in VCU software system to obtain the software to be tested.Then,the ECMS is optimized in real VCU and HIL tests are implemented accordingly.HIL simulation results show that improved fuel economy is achieved by adopting the optimized ECMS via IGA during charge sustaining period of battery.