| As an important direction of development of modern vehicles,plug-in hybrid electric vehicles(PHEVs)can merge advantages of pure electric vehicles(PEVs)and traditional internal combustion engine(ICE)vehicles,and features high fuel economy,low greenhouse gas(GHG)emission and long driving mileages.Energy management strategies(EMS)of PHEVs can directly influence the driving performance,fuel economy,and emissions.With the rapid development of global positioning system(GPS),geographic information system(GIS)and intelligent transportation system(ITS),the road and traffic information can be sufficiently taken into account when designing the EMS,thereby further improving the fuel economy of PHEVs.To optimize fuel savings and improve adaptation of the EMS to different driving conditions,a parallel PHEV equipped with a mechanical automatic transmission(AMT)is adopted as the research object in this study,and a fusion algorithm that incorporate transportation information and adaptive control is put forward to achieve the energy management.Main research findings are detailed as follows:(1)Different PHEV powertrain configurations are detailed and analyzed to compare their strengths and weaknesses,whereby the specification of the adopted hybrid system is determined,and its mechanical structure and operating principle are analyzed.The theoretical power demand of the vehicle is calculated according to requirement of the driving performance.By conducting statistical analysis of the operating data of taxis in a domestic city,the distribution of the power demand in real driving conditions is acquired.Main parameters of the key powertrain components are determined by combining distributions of the theoretical power demand and of the real requirements.On this basis,the PHEV powertrain system is modeled,by which the rationality of parameters is verified based on the dynamic performance simulation of the vehicle.(2)The driving data collected by experimental vehicles and standard driving cycle are clustered into four types based on the K-means clustering algorithm which is optimized by simulated annealing(SA).By considering the state of charge(SOC)of the battery and real driving distance,an equivalent distance factor is defined to demonstrate features of the driving distance.Meanwhile,the basic characteristics of the driving cycle is expressed by the road type and equivalent driving distance.Based on the components’ characteristics of the PHEV in this study,dynamic programming(DP)is adopted to search the global optimal energy management solution to the PHEV.Moreover,the basic control strategies under these four driving conditions are dynamically optimized.The influence on the optimal control strategy of the PHEV is analyzed with respect to different road types and equivalent driving distances,and consequently,the variation rules of starting power of the engine,torque distribution and shifting algorithms are extracted under different driving conditions.(3)The acquisition and processing manner for average speed information of the global traffic flow is studied,and the road network model is established to simulate variation of actual traffic flow.In addition,the average speed of the traffic flow is calculated,and real-time driving condition is effectively modeled.Focusing on the unavoidable missing problem of data in real operation,the K-nearest neighbor method is introduced to compensate the missed information of the average speed of the traffic flow.On this account,the wavelet transformation and smoothing filtering algorithm are applied together to generate the anticipated global transportation information during the whole trip.Simulation results highlight that the proposed algorithm can effectively compensate the missed transportation information and generate the anticipated global transportation information,thereby paving the road for research of the designed adaptive equivalent consumption minimization strategy(ECMS).(4)An adaptive ECMS(A-ECMS)energy management strategy is proposed based on the energy balance principle of the hybrid power system.To attain it,a pair of boundary equivalent factors are determined in advance according to the global traffic information,and then the real-time probability factor is obtained based on the real-time energy variation of the power system.The equivalent factor can be calculated in real-time with the help of the predetermined probability factor and boundary equivalent factors,thus realizing adaptive energy control of the powertrain system.The proposed energy management strategy is validated under three standard driving cycles with different battery aging status.Simulation results show that the proposed strategy performs superior adaptability and robustness in comparison with the traditional ECMS.The fuel economy can be improved by 5% to 11% in the condition of the battery degradation.(5)To validate the proposed A-ECMS for PHEV,a virtual traffic environment is adopted as the experimental platform and key construction framework of the platform is explained.In the system,the real-time simulation system,vehicle controlling system,driver manipulation system,virtual scene system and dynamic data monitoring system are integrated as an assemble,in which the transportation simulation system is built by a virtual scene software,i.e.,PreScan,together with a traffic flow simulation software VISSIM.In addition,D2 P is selected to conduct the vehicle control,and the NI-PXI platform is leveraged to simulate the variation of each component of the powertrain.Furthermore,the NI-Veristand is harnessed to dynamically monitor the experimental process.Based on the constructed simulation experiment platform,the detailed experiment scheme is devised and the hardware-in-the-loop(HIL)simulation experiment is conducted.Experimental results manifest the effectiveness of the proposed EMS of the PHEV with integration of traffic information and adaptive control. |
PDF Full Text Request