Research On Real-time A-ECMS For Plug-in Hybrid Electric Bus Considering Gear Shift Optimization

Hybrid technology is an energy-saving and environmentally friendly new energy technology with good development prospects.Plug-in hybrid electric bus(PHEB)is a new type of energy-saving and environmentally friendly public transportation vehicle that has emerged with the rapid development of the hybrid technology in recent years.Compared with traditional buses,PHEB has many advantages such as economical and practical,good comfort,and less pollution emission.Supported by the National Natural Science Foundation of China(51875261)and the Jiangsu Provincial Outstanding Youth Fund(BK20180046),many research studies are carried out such as PHEB longitudinal dynamics mathematical model,adaptive equivalent fuel consumption minimum strategy(A-ECMS),dynamic programming algorithm(DP),BP neural network vehicle gear fitting optimization,and hardware-in-the-loop test(HIL)based on NI Veri Stand platform.The main contents of this thesis are summarized as follows:1.The modeling method of PHEB is analyzed,and the forward modeling method of PHEB is selected.Based on the Matlab\Simulink platform,vehicle dynamics submodels are built,which includes the motor,engine,battery pack,tires,etc.The vehicle longitudinal dynamic model is verified through west virginia city driving cycle conditions(WVCITY).The accuracy of the longitudinal dynamic model is improved.2.The control principle of PHEB’s equivalent fuel consumption minimization strategy(ECMS)is analyzed.Aiming at the problem of poor adaptability to different working conditions and poor fuel economy caused by the fixed equivalent factor(EF)in traditional ECMS,an adaptive ECMS(A-ECMS)strategy is proposed,which introduces the grey wolf optimization algorithm(GWOA)to optimize the EF in realtime.By applying the division of working conditions and the calculation of the reference SOC decline trajectory,the segmental optimization solution to EF is realized,and the rationality of the strategy for the planning of electric energy usage is proved by software simulation under WVCITY and ECE driving conditions.3.The basic principles and modeling process of dynamic programming(DP)algorithm and BP neural network are expounded,and a BP neural network fitting gear model based on the calculation results of dynamic programming algorithm is proposed,which optimizes the gear shift sequence in the A-ECMS strategy.Combined with the A-ECMS strategy,an improved A-ECMS strategy considering gear optimization is designed.By utilizing the Matlab\Simulink platform,the results of the strategy in terms of torque distribution,power tracking and engine operating point selection are demonstrated,and the fuel economy results of the proposed strategy are verified under mixed driving conditions.4.Based on the NI Veri Stand platform and Matlab\Simulink platform,the construction of the longitudinal dynamics model and energy management controller of the PHEB controlled object vehicle is completed,and the CAN communication protocol between the physical controller and the host computer was configured.The signal transmission between the controller and the controlled object is realized,and the closed-loop simulation experiment of the PHEB energy management controller is carried out.By comprehensively comparing the results of HIL test and software simulation,it is verified that the A-ECMS strategy based on gear optimization can meet the power requirements of the vehicle.The reliability of the strategy developed on the HIL test platform is proved by the experimental results.