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.

scholarly journals Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1650 ◽  
Author(s):  
Bong-Gi Choi ◽  
Byeong-Chan Oh ◽  
Sungyun Choi ◽  
Sung-Yul Kim
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Traffic Load ◽  
Queueing Model ◽  
Traffic Data ◽  
Electric Vehicle Charging ◽  
Supply Equipment ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

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 A data-driven approach for characterising the charging demand of electric vehicles: A UK case study

2016 ◽  
Vol 162 ◽  
pp. 763-771 ◽  
Author(s):  
Erotokritos Xydas ◽  
Charalampos Marmaras ◽  
Liana M. Cipcigan ◽  
Nick Jenkins ◽  
Steve Carroll ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Data Driven ◽  
Data Driven Approach ◽  
Charging Demand

scholarly journals An Electric Taxi Charging Station Planning Scheme Based on an Improved Destination Choice Method

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3781 ◽  
Author(s):  
Shi ◽  
Liu ◽  
Liao ◽  
Niu ◽  
Ibrahim ◽  
...  
Keyword(s):  
Public Transportation ◽  
Environmental Crisis ◽  
Chinese Government ◽  
Operation System ◽  
Charging Station ◽  
Fast Charging ◽  
Planning Scheme ◽  
Charging Stations ◽  
Ev Charging

The environmental crisis has prompted the development of electric vehicles as a green and environmentally friendly mode of travel. Since a reasonable layout of electric vehicle (EV) charging stations is the prerequisite for developing the EV industry, obtaining an optimal and efficient EV charging station planning scheme is a key issue. Although the Chinese government has carried out a plan to build EV charging piles in residential and working places, it cannot properly fulfill the task of matching the charging needs for public transportation vehicles such as electric taxis (ETs). How to evaluate the performance of fast charging stations (FCSs) and how to help find the optimal ET charging station planning scheme are new challenges. In this paper, an improved destination selection model is proposed to simulate the ET operation system and to help find the optimal ET charging station size with statistical analysis based on the charging need prediction. A numerical case study shows that the proposed method can address ET charging behavior well and can help to statistically determine the size of each ET charging station, which should satisfy the constraints on the preset proportion of the ET charging service requests.

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

Siting and sizing of fast charging stations in highway network with budget constraint

2018 ◽  
Vol 228 ◽  
pp. 1255-1271 ◽  
Author(s):  
Yue Wang ◽  
Jianmai Shi ◽  
Rui Wang ◽  
Zhong Liu ◽  
Ling Wang
Keyword(s):  
Budget Constraint ◽  
Fast Charging ◽  
Highway Network ◽  
Charging Stations

scholarly journals Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2479 ◽  
Author(s):  
Yue Wang ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Rui Wang
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Optimal Policy ◽  
Practical Case ◽  
Battery Electric Vehicle ◽  
Battery Electric Vehicles ◽  
Endurance Range ◽  
Highway Network ◽  
Charging Stations ◽  
The Impact

The promotion of the battery electric vehicle has become a worldwide problem for governments due to its short endurance range and slow charging rate. Besides an appropriate network of charging facilities, a subsidy has proved to be an effective way to increase the market share of battery electric vehicles. In this paper, we investigate the joint optimal policy for a subsidy on electric vehicles and infrastructure construction in a highway network, where the impact of siting and sizing of fast charging stations and the impact of subsidy on the potential electric vehicle flows is considered. A new specified local search (LS)-based algorithm is developed to maximize the overall number of available battery electric vehicles in the network, which can get provide better solutions in most situations when compared with existed algorithms. Moreover, we firstly combined the existing algorithms to establish a multi-stage optimization method, which can obtain better solutions than all existed algorithms. A practical case from the highway network in Hunan, China, is studied to analyze the factors that impact the choice of subsidy and the deployment of charging stations. The results prove that the joint policy for subsidy and infrastructure construction can be effectively improved with the optimization model and the algorithms we developed. The managerial analysis indicates that the improvement on the capacity of charging facility can increase the proportion of construction fees in the total budget, while the improvement in the endurance range of battery electric vehicles is more efficient in expanding battery electric vehicle adoption in the highway network. A more detailed formulation of the battery electric vehicle flow demand and equilibrium situation will be studied in the future.

scholarly journals Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 178
Author(s):  
Gilles Van Van Kriekinge ◽  
Cedric De De Cauwer ◽  
Nikolaos Sapountzoglou ◽  
Thierry Coosemans ◽  
Maarten Messagie
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Negative Impact ◽  
Absolute Error ◽  
Electric Grid ◽  
Electric Vehicle Charging ◽  
The Neural Network ◽  
The Mean ◽  
Ev Charging ◽  
Charging Demand

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.

Sign in / Sign up

Export Citation Format

Share Document