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

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1412 ◽  
Author(s):  
Qiang Xing ◽  
Zhong Chen ◽  
Ziqi Zhang ◽  
Xiao Xu ◽  
Tian Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Electric Vehicles ◽  
Demand Forecasting ◽  
Model Based ◽  
Fast Charging ◽  
Human Decision ◽  
Data Driven Approach ◽  
Demand Forecasting Model ◽  
Decision Making Behavior ◽  
Charging Demand

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.

scholarly journals Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137390-137409 ◽  
Author(s):  
Qiang Xing ◽  
Zhong Chen ◽  
Ziqi Zhang ◽  
Xueliang Huang ◽  
Zhaoying Leng ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Demand Forecasting Model ◽  
Charging Demand

scholarly journals Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2428
Author(s):  
Abood Mourad ◽  
Martin Hennebel ◽  
Ahmed Amrani ◽  
Amira Ben Hamida
Keyword(s):  
Electric Vehicles ◽  
Production Costs ◽  
Heavy Trucks ◽  
Fast Charging ◽  
Highway Network ◽  
Data Driven Approach ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

The need for deploying fast-charging stations for electric vehicles (EVs) is becoming essential in recent years. This need is justified by the increasing charging demand and supported by new charging technologies making EV chargers more efficient. In this paper, we provide a survey on EV fast-charging models and introduce a data-driven approach with an optimization model for deploying EV fast-chargers for both electric vehicles and heavy trucks traveling through a network of suburban highways. This deployment aims at satisfying EV charging demands while respecting the limits imposed by the electric grid. We also consider the availability of local photovoltaic (PV) farm and integrate its produced power to the proposed charging network. Finally, through a case study on Paris-Saclay area, we provide locations for EV charging stations and analyze the benefits of integrating PV power at different prices, production costs and charging capacities. The obtained results also suggest potential enhancements to the charging network in order to accommodate the increasing charging demand for EVs in the future.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

Perspectives on Decision Analysis for Decision Support System Design

1993 ◽  
Vol 37 (16) ◽  
pp. 1101-1101 ◽  
Author(s):  
Barbara J. Barnett
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
System Design ◽  
Support System ◽  
Information Organization ◽  
Human Decision ◽  
Tactical Decision ◽  
Decision Making Behavior

This symposium addresses the characterization of human decision making within a complex environment for the purpose of developing improved decision support systems. All of the work presented in this symposium was conducted under a Navy research program entitled “Tactical Decision Making Under Stress” (TADMUS). The overall objective of the TADMUS program is to improve tactical decision making of anti-air warfare (AAW) crew members within the Aegis cruiser's combat information center (CIC) under conditions of stress and uncertainty. The unique aspect of this effort is that each presentation addresses decision making behavior, within a single domain, from a different perspective. The goal of each effort is to characterize some aspect of expert decision making performance within the AAW task environment, and to make recommendations for the resulting decision support system design based upon these characterizations. The result is a multi-faceted, human-centered approach to information organization and interface display design for a decision support system.

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