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

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.Energies 2020, 13, 1412 2 of 32 331,000 public charging piles and 477,000 private charging piles in 2018, an increase of 74.2% over the same period in 2017 [6].However, the driving and charging behaviors of numerous EVs in urban internal networks are bound to interact with the energy and information generated by the traffic network and power grid [7,8]. The residents' trip rule, urban road network structure and charging facility distribution affect the vehicle driving distribution and charging selection, whereas the vehicle battery parameters, driving path plan and human decision-making behavior also affect the degree of traffic network obstruction and the power grid operation state [9][10][11][12]. Therefore, accurate prediction of EV charging demand and reasonable fast-charging station (FCS) recommendations are the premise of realizing compatibility between EVs and the power grid along with the transportation network.At present, various studies have developed EV charging demand models from the aspect of cooperation between EVs, the transportation system and power system all together. Hence, 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. Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior o...

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

“…With the idea in [27,28], the power regulation of CS can meet the load reduction requirement by finding the optimal combination of charging piles to control. With the objective of minimum deviation between the demand and the response, the optimal numbers for controlling in each cluster can be obtained according to (20) and (21).…”

“…Constraint (21) means that the number of charging piles participating in control within a time interval cannot exceed the total number in corresponding clusters. R k (t) is the peak shaving demand in the CS at period t, and the unit is kW.…”

“…e charging profiles of PEVs have large volatility [20] which require the robustness of the clustering method. Even for the same CS, there are load peaks with different amplitudes at different occurrence times [21]. During the clustering process, the largest challenge is the high variability of the load patterns.…”

Fast charging load guidance strategy based on adjustable charging service fee

“…Based on the interaction of grid operation state and road network traffic information, [6] constructed a fast‐charging load forecasting model comprehensively considering the influence of road‐electric coupling on EV trip rules and obtained the spatial and temporal distribution characteristics of fast‐charging load under aggregated state. In [7], an EV fast‐charging demand forecasting model was proposed based on data‐driven and user charging decision‐making behaviours to analyse users’ decisions based on regret theory, and finally the spatial and temporal distribution of fast charging loads was obtained. In the literature mentioned above, when forecasting the charging load, it is believed that EVs are recharged at the destination at the end of the trip, taking into account the length of the parking time and the remaining battery energy to choose fast or slow charging, which lacks in‐depth research of the generating location of users’ charging needs and psychological conditions.…”

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

“…With the increasingly severe problems of fossil energy consumption and environmental pollution, the transportation industry is concerned by governments worldwide. As an environment-friendly means of transportation, EVs usher in development opportunities [1,2]. By 2020, the number of new energy vehicles in China has reached 4.92 million, accounting for 1.75% of vehicles.…”