Abstract
Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the ‘Electric Vehicles–Power Grid–Traffic Network’ fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners’ charging behavior.
Highlights
As a cleaner means of transportation that is low-carbon and environmentally friendly, electric vehicles (EVs) can reduce greenhouse gas emissions [1,2], but they improve country strategies to secure energy [3]
Residents travel and vehicles drive on traffic networks, so the dynamic evolution process of traffic can be vividly depicted by traffic network modeling
Are shown in Figure information of of each each fast-charging station is is are shown in Figure 4, 4, wherein wherein geographical geographical location location information fast-charging station shown in the
Summary
As a cleaner means of transportation that is low-carbon and environmentally friendly, electric vehicles (EVs) can reduce greenhouse gas emissions [1,2], but they improve country strategies to secure energy [3]. In [13,14], an origin destination (OD) matrix analysis method was utilized to track the all-weather driving trajectory of EVs and to predict the charging load distribution of the regional electricity grid as well as the flow status of road networks through vehicle traffic trip demands. On the basis of our existing research [37], this paper continues to combine data mining technology with human behavior decision-making modeling, and it proposes a fast-charging demand forecasting model for urban EVs. First of all, Didi original order trajectory data are modeled and analyzed, and the regenerative characteristic data needed for traffic operation are obtained through data mining and fusion technology.
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