The Green Wave Band-based Energy Consumption Analysis And Path Planning For Electric Vehicles

The increasing per capita vehicle ownership not only makes more traffic jams,but also brings extremely urgent environment problems,such as energy crisis,haze and so on in every year.In order to avoid becoming worse from these situations,the development of new energy vehicle is booming and the automobile industry,which has the most direct impact on environment has already gradually entered a comprehensive transformation period of traffic energy.Electric vehicles,namely EVs,as the typical representative of new energy vehicles,are dedicated to realize the goal of zero emission so that this is able to relieve the dilemma which is caused by the insufficient combustion and low utilization ratio of fossil energy on conventional vehicles.However,such challenges within EVs like limited battery capacity and shorter journey distance are the main reasons to restrict their market penetration ratio.Therefore,it is pretty meaningful for EVs to take full advantage of limited energy by using the existing urban traffic control theory to model and analyze their energy consumption,which may make up the defects mentioned above and it will be fairly helpful for EVs to acquire a further penetration ratio,change the traffic composition and improve the environment pollution.Firstly,this thesis proposes a novel kind of energy consumption model for EVs,which is based on traffic signal control theory and combined with the distribution of energy consumption in a macroscopic view.Namely,a green wave band-based model for energy consumption of EVs,also can be titled by Green wave band-based Energy Model,GEM for short.GEM models green wave scenarios with the realistic data of traffic signals and involves specific parameters like offset and green split for energy consumption of EVs,which aims to demonstrate the positive influence of green wave band in the aspect of energy consumption.By using VISSIM and ADVISOR,which are a microscopic simulator for traffic and a professional simulation software for EVs respectively,corresponding outputs show that the energy saving and efficiency improvement are available and feasible for EVs under the proposed model.In addition,based on the proposed model,this thesis puts forward an optimization algorithm,named Green wave band-based Multi-objective Ant Colony Optimization algorithm,GMACO for short.This algorithm focuses on the multi-objective cost issue of EVs when they are travelling.It can search and recommend paths for EVs when considering energy consumption and time cost which impact the process of travelling.After reconstructing and designing the related weight in an abstract road network,we use Matlab to validate GMACO.The simulation results demonstrate that G-MACO is able to satisfy the drivers' need both in energy consumption and time cost appropriately in terms of subjectivity and objectivity.Meanwhile,it can make EVs use urban roads with green wave effect reasonably and make a tradeoff on energy consumption and time cost.For EVs,it achieves the effect of multi-objective optimization at last.It can be seen that the effect of green wave is a nonnegligible and advantageous factor for EVs in the aspect of energy efficiency,which is able to further improve the energy efficiency of EVs.Besides,this effect plays a vital role in the path planning of EVs and has important practical significance.