Research On Green Taxi Trajectory Optimization Of Airport Surface

Along with the booming development of air transportation industry in our country,the volume of flights continues to increase,bringing a more complex of aircraft taxiing scheduling in the airport surface,and the emission of pollutant exhaust during scheduling will also affect the airport and surrounding environment.Firstly,according to the aircraft’s motion characteristics and taxiing force,this paper establishes the taxiing model based on taxi time and fuel consumption.For the single and fixed taxiing trajectory,the specific correlation between aircraft taxi time and fuel consumption is analyzed from the point of taxiing speed optimization.This paper uses the non-dominated sorting genetic algorithm II(NSGA-II)to analyze the pareto front between taxi time and fuel consumption and put forward the most time efficient and most fuel consumption efficient strategy.Then,within level of speed optimization,the optimization of taxi route is added.The activing routing framework generates multiple taxi routes for aircraft and assign the optimized speed profile based on these routes.To solve the trade-off problem between time and fuel consumption,the taxi cost is introduced and establishes the model of taxi cost.The k-shortest path algorithm is used for simulation.The results review that the strategy can effectively reduce taxiing time and fuel consumption,and decrease the taxi cost.Finally,aiming at the emission problem from aircraft taxiing,the emission is add in optimization objective.By comprehensive analysis of the effects taxiing thrust and fuel flow on the emission index,the exhaust emission is introduced into the taxi model and proposed the exhaust emission optimal strategy.According to the simulation,this strategy can optimize both exhaust emission and fuel consumption,and achieve the scheduling with green taxiing trajectory.The prospect summarizes the shortcomings of the research and indicate the future research direction,which will have significance to improve the operational efficiency of taxiing scheduling.