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《China Journal of Highway and Transport》 2017-10
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Multi-segment Green Light Optimal Speed Advisory for Hybrid Vehicles

LUO Yu-gong;HU Shu-mang;ZHANG Shu-wei;State Key Laboratory of Automotive Safety and Energy,Tsinghua University;  
The purpose of this paper is to improve the traffic efficiency of vehicles,and solve the problem of extra energy consumption and forced stop at signalized intersections in the urban area caused by the unreasonable speed control. In allusion to the current situation where the rule-based methods and simple velocity model were mainly applied in related researches to optimize and solve the speed curves,and the speed optimization of vehicles was limited to the traditional internal combustion engine vehicles,neglecting the potential of the energy management of hybrid vehicles in the future,an approach aiming to optimize green light optimal speed advisory( GLOSA) and energy management for hybrid vehicles based on genetic algorithm was proposed. Meanwhile,the scene model of GLOSA and the multi-constrained nonlinear optimization equations in view of varying accelerated model were established. The speed curve and the energy management of the hybrid vehicles were optimized simultaneously by dint of the optimization of acceleration of the vehicle and torque distribution relationship of energy sources. The results show that,at low vehicle speed,the efficiency of the engine calculated by general methods of GLOSA is low. The extra energy consumption is caused by extra travel time. Under the condition that the green light interval is ensured,the total fuel consumption decreases by 5%-29% the optimal energy management of hybrid vehicles is achieved by the proposed method. Meanwhile,the travel time decreasesby 20. 4% in simulation.
【Fund】: 国家自然科学基金项目(51575295)
【CateGory Index】: U491
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