Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

Qiang Xing, Zhong Chen, Ziqi Zhang, Xiao Xu, Tian Zhang, Xueliang Huang and Haiwei Wang
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Qiang Xing: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Zhong Chen: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Ziqi Zhang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Xiao Xu: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Tian Zhang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Xueliang Huang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Haiwei Wang: State Grid Anhui Electric Power Company Electric Power Research Institute, Hefei 230088, China

Energies, 2020, vol. 13, issue 6, 1-32

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.

Keywords: electric vehicles; fast-charging demand forecasting; ride-hailing trip data; data mining and fusion; human behavior decision-making; Regret Theory model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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