| Taxi is a fatal component of public transportation,and improving the penetration of electric vehicles in the taxi industry is effective for energy conservation and emission reduction.In order to solve the problem of scale optimization of electrification,this paper first proposed a discrete simulation framework integrating the behavior characteristics of electric taxi,and based on the simulation results,proposed a multiobjective optimization model of the scale of electrification.In view of the problems brought by "one-size-fits-all" undifferentiated electrification,this paper quantifies the differences of taxi drivers’ adaptability to electric vehicles from two aspects of operating habits and driving habits,and evaluates the drivers by using the grey relational degree evaluation method with various adjustment coefficient.To sum up,the main research contents of this paper are as follows.(1)The discrete-event simulation framework is designed according to behavioral characteristics of electric taxis.The simulation framework should first build the logic loop of the system dynamic evolution,and fill in the simulation rules of a series of possible actions of the electric taxi.In addition,the framework should also have the functions of data preprocessing,real-time data recording,and data analysis after the simulation.Based on this framework,the simulation of the system state under different electrification scale is carried out.The result shows that: The increase in electrification scale makes the operating index value gradually decrease;Unmet travel demands first disappear in the regions with large demands.The current charging facilities are far from realizing complete electrification;Whether an electric vehicle can generally find a suitable charging station within three searches can be a basis to measure the adequacy of charging facilities.(2)A multi-objective optimization model was established to figure out the optimal electrification scale.The optimization objectives involve the satisfaction degree of passengers’ travel demands,the operating income of the drivers,and the service pressure of the charging facilities.Then,the MOPSO algorithm was adopted to solve the model,and the optimal electrification scale under the set charging facility configuration(quantity and spatial distribution of charging facilities)was obtained as778.Under the optimal scale,each component of the system can be kept in a relatively ideal state.And the travel demands of residents in Zhengzhou that are met by gasoline taxis could be met by electric taxis too.The charging peak time is around 15:00～18:00,and many charging stations are able to handle charging demands generated by 778 electric taxis.(3)Adaptability evaluation of drivers to electric vehicles.The characteristics of drivers including the economy of driving habits,charging convenience,distance per trip,and empty-loaded rate are extracted.Then the grey correlation analysis method is adopted to evaluate the driver’s electrification fitness,and a defined adjustment coefficient of indexes’ variance is added into the calculation to get more adapted to the research.Motorization simulation was carried out for drivers with high and low fitness to quantify the influence of fitness on operation process.The experiment showed that the mileage and time usage of high fitness drivers is higher than low-fitness drivers.But in terms of the high fitness group,the usage decreases after electrification.For such kinds of active drivers,the disadvantages show stronger impact in the operation process,namely under the condition of the vehicle’s performance,this kind of driver’s loss will be bigger.These drivers with high fitness are active in operation and have more charging demands,and these charging demands are more spatially intensive and closer to the distribution position of charging stations. |
