Characteristic Analysis And Estimation For Exhaust Emissions Of New-energy Buses With Multi-source Data

In recent years,with the continuous advancement and development of urbanization,the living standards of residents have been continuously improved,and the number of motor vehicles in China has continued to increase,which has caused the deterioration of the operation status of urban transportation system in China,and the problem of motor vehicle exhaust emissions and environmental pollution has become increasingly serious.In order to reduce transportation energy consumption and exhaust emissions,China vigorously develops public transportation systems,especially new-energy buses are gradually replacing traditional fuel buses.Although new-energy buses have the characteristics of low energy consumption and low emissions,they will still emit greenhouse gas and pollutant gas emissions.However,there are relatively few studies on the analysis and estimation methods for the emission characteristics of new-energy buses:lack of division of operating bins and analysis of emission characteristics for new energy buses;lack of consideration of the impact of bus stops on emission characteristics of buses from the perspective of spatiotemporal correlation;and lack of new energy bus emission rate estimation models at bus line nodes.In light of this,taking diesel buses(DB)as a comparison benchmark,this paper systematically analyzes and evaluates the emission impacts of liquefied natural gas bus(LNGB),hybrid electric bus(HEB)and battery electric bus(BEB),including driving conditions analysis,comparison of emission characteristics,emission rate and emission factor estimation,influencing factor analysis and evaluation methods,etc.,and put forward corresponding policy recommendations for our cities,as well as provide theoretical support for public transportation planning,layout,management and control.The main research work of this paper can be divided into the following five aspects.(1)Multi-source data acquisition and preprocessing.In order to achieve the objective of this paper,we collected the multi-source dataset,which includes GPS and emission data for buses in real-world driving,dynamic passenger load data,POI(Point of interest)data,traffic flow data,and bus stop feature data.This paper details the data types and formats of each type of dataset,and preprocesses the data using cleaning,interpolation,matching and fusion techniques,which lays a data foundation for the follow-up urban bus emission characteristic analysis,influencing factor investigation,emission rate estimation and life-cycle emission intensity assessment.(2)Operating Bins classification and emission analysis.Based on the measured emission data,the traditional vehicle specific power(VSP)method was used to analyze the emission characteristics for new-energy buses and compared with the traditional diesel buses.Through the data analysis,a method for classifying the driving operating bins based on both speed and VSP is proposed,and the effectiveness of the method is validated by comparing the emission measurement values of the training data set and the test data set.Using this method,the emission characteristics of four types of exhaust emissions(Carbon monoxide(CO),Carbon dioxide(CO₂),Hydrocarbon(HC),Nitrogen and oxygen(NOx))of three types of buses under different driving conditions are analyzed,and the emission differences under the same driving conditions are compared to estimate each type of bus.The emission levels of vehicles under different operating bins lay the foundation for the life cycle assessment in this paper.(3)Spatial variation characteristics for the bus stop influence zone.The length of the bus stop influence zone is the basis for estimating the emission level of urban buses at bus stops,and the changing characteristics of this length may be affected by many factors.In view of this,this paper studies how the urban built environment affects the length of the bus stop influence zone from the perspective of urban space.Based on the matching and fusion of bus emission data,GPS data,POI data,and traffic flow data,a geographically weighted regression(GWR)model was proposed to establish the relationship between the length of the bus stop influence zone and various influencing factors.The results show that the shape of the city has a significant effect on the length the bus stop influence zone,and the length of the bus stop influence zone has a strong spatial heterogeneity.The number of enterprises and companies near bus stops,the distance between bus stop and intersection,the road level,the number of public facilities,the length of the bus queue,and the peak hour traffic volume of the road where the bus stop is located will all significantly affect the length and estimated coefficient of bus stop influence zone.The research results provide a theoretical basis for the planning and layout of bus stops and help to quantify and estimate the emissions generated near bus stops.(4)Real-time estimation of CO₂ emission rate for new-energy buses.Considering the impact of bus line nodes(bus stops,intersections)on the emission characteristics of buses,the road location is divided into three parts:bus stops,intersections,and road sections,and statistical analysis methods are used to compare the variation characteristics of the CO₂exhaust emission rate generated under the three road location.The correlation analysis and feature selection methods are employed to determine the important factors affecting the carbon dioxide emission rate,a XGBoost machine learning model is proposed for estimating the carbon dioxide exhaust emission rate.The results show that the XGBoost machine learning model has a higher degree of estimation accuracy compared with traditional regression methods and machine learning methods.Compared with diesel buses,this model has higher performance in estimating the real-time emission rate for new-energy buses,indicating that the accurate estimation of CO₂ emission rate can be achieved.(5)Life cycle assessment for emissions of battery electric bus.In order to effectively quantify and evaluate the emission intensity and environmental protection characteristics of battery electric buses(BEB),a data list of the mining,production and processing of relevant fuels and materials in China was collected.Based on the GREET model,from the perspective of life cycle,the total energy consumption intensity,greenhouse gas and pollutant gas emission intensity of the vehicle manufacturing and assembly stage,fuel mining,processing and transportation stage,and fuel consumption stage are evaluated respectively.The results show that although BEB have the advantage of zero emissions during operation,the current battery technology and power composition in China make the vehicle's energy consumption and emissions insignificant compared to traditional internal combustion engine buses throughout the life cycle.Even for some polluting gases,such as nitrogen oxides and sulfur oxides,the emission factors of BEB are significantly higher than traditional internal combustion engine buses.However,with the continuous development of battery technology and the gradual optimization of power structure in China,the advantages of BEB in terms of energy saving and emission reduction will become increasingly obvious,and it will also be the most important type of ground public transportation in China in the future.Based on the obtained multi-source data,this paper conducted a comprehensive study on the classification of operating bins,analysis of emission characteristics,analysis of influencing factors of bus stop emissions,real-time estimation of emission rates at nodes,and evaluation of environmental characteristics of battery electric buses.It provides an important reference for fully understanding the emission characteristics of new energy buses,provides a theoretical basis for the planning and layout of bus stations,provides a reference method for real-time monitoring and management of emissions in China,and also provides theoretical support and policy recommendations for bus configuration and planning in China.