Modeling And Simulation Of A Plug-in Parallel Hybrid Electric Bus

With the development of the global automobile industry, the yield, sale quantity and inventory quantity of the automobile are increasing year by year, the problems of energy short and environment pollution are becoming more and more serious, and the new energy automobile becomes the hotspot of the automobile company and research institution. At present, Plug-in Hybrid Electric Vehicle is one of the new energy automobile research spots.The Plug-in Hybrid bus was researched,the advantages and disadvantages of PHEV and the work feature of PHEV were principally introduced, and the single axle and dual-clutch structure was chosen for the Plug-in city bus. A simulation model based on Matlab/Simulink was modeled,which can run on simulation platform of ADVISOR. The electric assistant control strategy of Parallel hybrid vehicle was researched. According to the design demands, the main parameters of the Plug-in city bus were calculated and matched, the control strategy combined the configuration of the Plug-in city bus with the Chinese typical city bus road condition was established. The parameters of control strategy were designed by using orthogonal designing experiment based on average running mileage 180km (30 Chinese typical bus driving cycles)of the city bus, ultimately, the combination of optimal control parameters was confirmed based on the minimum fuel consumption.According to the establishing model, the acceleration performance,climbing performance,max speed,the vehicle fuel consumption per hundred kilometers on the Chinese typical city bus road condition, the working point distribution map of engine and motor were obtained. The results show that engine and motor is worked in good condition, which is worked in the optimal efficiency zone when meeting the demand of power performance and fuel economy. The proper power distribution, start and stop of engine and motor, brake energy feedback, as well as maintaining SOC in the enactment zone can be realized reasonably based on control strategy; the battery's capacity can meet the demand of range. fuel consumption is reduced obviously by using orthogonal designing optimization for control parameters. The fuel consumption is more lower after the parameters of control strategy was optimized by orthogonal experiment designing.