Research On The Eco-Driving Strategy Of Urban Road Based On The Cooperation Vehicle Infrastructure System

With the rapid development of automobile industry, the quantity of vehicles in our country has a sharply growing, which is great convenient for people to travel. However, this causes several problems more and more seriously such as traffic congestion, energy consumption and emission pollution, which has been paid more and more attention by scholars and researchers. In the long-term study, researchers have found that many factors affect the energy consumption of the transport system, and the driving behavior is one of the important factors; the eco-driving behavior can bring significant reduction in fuel economy and emissions pollution. With the continuous upgrade of technology, an increasing number of modern techniques and devices are widely used in traffic system, which provides an important guarantee for the benign development of the traffic system. Cooperative vehicle infrastructure system(CVIS) is one of the most important typical techniques.CVIS gets the vehicle-road information based on the techniques such as wireless communication and sensors detecting. CVIS could accomplish the intelligent coordination between vehicle and infrastructure, and also achieve the goal of optimizing the resources. An eco-driving strategy that adapts to actual driving operation is established by CVIS, and drivers could keep driving according to the signal phase and timing(SPa T) and the strategy. Then the fuel consumption and emission will significantly reduce.In this paper, a model of eco-driving strategy that adapts to the actual driving operation is established, by analyzing and summarizing the existing eco-driving strategies at home and abroad, and using the real vehicle test data, the BP neural network, the VISSIM simulation, etc.The main research contents are as follows:Firstly, the eco-driving strategy that adapts to the actual driving operation is proposed, and the real vehicle test data is collected, through analyzing the research results of eco-driving strategy based on CVIS. Then the collected test data is processed and analyzed to provide data support for further research.Secondly, the speed prediction model based on BP neural network is established. The test data is used to train the model, and then the testing program is predicted, thus getting the corresponding real vehicle driving strategy.Thirdly, the eco-driving scheme is obtained by curve fitting and integral operation to get. The curve fitting and integral operation are carried out on the real vehicle driving strategy, thus getting one or more eco-driving schemes for each testing program.Finally, the eco-driving strategy model is established to get the eco-driving strategy. According to the v-t curve by curve fitting, the eco-driving strategy model is established by analyzing the relationship between the vehicle state and the signal timing. VISSIM is used to simulate the eco-driving scheme, and then the best eco-driving strategy is obtained by comparing the fuel consumption of each program. The selection principle of eco-driving strategy is as follows: If there is only one driving scheme, this scheme is the eco-driving strategy. Otherwise, the driving scheme of longest driving scheme at a constant speed and lowest fuel consumption should be selected as the eco-driving strategy.In this paper, within vehicle infrastructure integration environment, the eco-driving strategy based on the actual driving operation is established, by classifying and processing the real vehicle driving strategy. This thesis could provide an effective support for the driving strategy, and lay a theoretical foundation for ecological travelling and green driving.