Abstract
Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents’ transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs.
Highlights
With the adjustment of the world energy industry structure and people’s constant attention to environmental issues, the environmental pollution and reliance on resources of fossil fuels caused by the development of automobile industry are all challenging concerns of the present world.Several research results show that automobile exhaust emissions are the main source of urban air pollution [1], especially PM2.5
DATA VISUALIZATION Since our research mainly focused on the analysis of spatiotemporal distribution field information of the trip dataset, the required data were visualized after data preprocessing
In the light of (6), the proportions of Point of Interest (POI) data points in various functional areas were calculated as follows: residential areas 31.54 %, commercial areas 25.31 %, industrial areas 23.60 %, public service areas 15.76 %, road facilities areas 2.34 %, and green-land square areas 1.45 %, respectively. It can be seen from the analysis that the first four types of functional areas account for up to 96.21 %, due to the latter two types of sample data are too few, this paper mainly focuses on the first four types for the following functional area clustering research
Summary
With the adjustment of the world energy industry structure and people’s constant attention to environmental issues, the environmental pollution and reliance on resources of fossil fuels caused by the development of automobile industry are all challenging concerns of the present world. Tang and Wang [16] established a spatio-temporal model of moving EV load based on random trip chain and Markov decision process (MDP), and assessed the impacts on charging demand of grid nodes and traffic nodes due to the time-spatial distribution of moving EVs. Tao et al [17] utilized Monte Carlo method to draw the trip chain of an EV with all-weather driving, and developed a charging demand prediction probability model to evaluate the negative impacts of charging load on the power grid under different actual scenarios. This paper started with the data-driven aspect to mine and analyze Didi online ride-hailing trip dataset, and proposed a charging demand prediction model for EVs. The first step was to conduct data modeling for the original Didi trip dataset, and obtain the regenerative feature data such as functional regional division, trip rule distribution and actual driving path via the data mining and fusion technology.
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