Synthetic Performance Simulation of E-Car HS2000

The simulation model of the E car HS2000 including the permanent magnetic direct current motor with the augment magnet winding is constructed based on tests in order to simulate the synthetic performance of the electric car. The performance of E car HS2000 is analyzed by means of modeling and programming according to data acquired during tests. The simulation results show that the performance of E car HS2000 is successfully predicted and the model and the corresponding simulation software are feasible for simulating E cars. They can be used as effective tools for analyzing the performance parameters as well as specifications of E cars during prototype stage.

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.