Optimal locations of U.S. fast charging stations for long-distance trip completion by battery electric vehicles

2019 ◽  
Vol 214 ◽  
pp. 452-461 ◽  
Author(s):  
Yawei He ◽  
Kara M. Kockelman ◽  
Kenneth A. Perrine
Keyword(s):  
Electric Vehicles ◽  
Long Distance ◽  
Battery Electric Vehicles ◽  
Fast Charging ◽  
Charging Stations

Impact of Electric Vehicle Charging Infrastructure on the Distribution Network Power Quality

2022 ◽  
pp. 114-132
Author(s):  
Gagandeep Sharma ◽  
Vijay K. Sood
Keyword(s):  
Power Quality ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Control Strategies ◽  
Charging Infrastructure ◽  
Battery Electric Vehicles ◽  
Electric Vehicle Charging ◽  
Fast Charging ◽  
Charging Stations

This chapter discusses the available charging systems for electric vehicles (EV) which include battery electric vehicles (BEV) and plugged hybrid electric vehicles (PHEV). These architectures are categorized as common DC bus charging (CDCB) station and common AC bus charging (CACB) station. CACB charging stations are generally used as slow chargers or semi-fast chargers (on-board chargers). CDCB charging stations are used as fast chargers (off-board chargers). These chargers are vital to popularize the electric vehicles (EVs) as a green alternative to the internal combustion engine (ICE) vehicles. Further, this chapter covers the power quality problems related to the grid-connected fast charging stations (FCS), AC-DC converter, control strategies for converters, proposed system of architectures, methodology, system results with comparisons, and finally, a conclusion.

scholarly journals Electric Vehicle Fast Charging Needs in Cities and along Corridors

2019 ◽  
Vol 10 (2) ◽  
pp. 45 ◽  
Author(s):  
Rick Wolbertus ◽  
Robert Van den Hoed
Keyword(s):  
Inner City ◽  
Electric Vehicles ◽  
Urban Areas ◽  
Long Distance ◽  
Actual Usage ◽  
Fast Charging ◽  
Usage Patterns ◽  
User Groups ◽  
Charging Stations ◽  
Roll Out

Fast charging is seen as a means to facilitate long-distance driving for electric vehicles (EVs). As a result, roll-out planning generally takes a corridor approach. However, with higher penetration of electric vehicles in urban areas, cities contemplate whether inner-city fast chargers can be an alternative for the growing amount of slow public chargers. For this purpose, more knowledge is required in motives and preferences of users and actual usage patterns of fast chargers. Similarly, with increasing charging speeds of fast chargers and different modes (taxi, car sharing) also switching to electric vehicles, the effect of charging speed should be evaluated as well as preferences amongst different user groups. This research investigates the different intentions and motivations of EV drivers at fast charging stations to see how charging behaviour at such stations differs using both data analysis from charging stations as a survey among EV drivers. Additionally, it estimates the willingness of EV drivers to use fast charging as a substitute for on-street home charging given higher charging speeds. The paper concludes that limited charging speeds imply that EV drivers prefer parking and charging over fast charging but this could change if battery developments allow higher charging speeds.

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.

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.

SURVEY OF HARMONIC AND SUPRAHARMONIC EMISSION OF FAST CHARGING STATIONS FOR ELECTRIC VEHICLES IN CHINA AND GERMANY

2021 ◽  
Author(s):  
F. Chen ◽  
Q. Zhong ◽  
H. Zhang ◽  
M. Zhu ◽  
S. Müller ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Fast Charging ◽  
Charging Stations

scholarly journals Deploying Electric Vehicle Charging Stations Considering Time Cost and Existing Infrastructure

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2436 ◽  
Author(s):  
Yuan Qiao ◽  
Kaisheng Huang ◽  
Johannes Jeub ◽  
Jianan Qian ◽  
Yizhou Song
Keyword(s):  
Electric Vehicles ◽  
Optimization Algorithm ◽  
Central Area ◽  
Time Cost ◽  
Battery Electric Vehicles ◽  
Electric Vehicle Charging ◽  
Service Areas ◽  
Harsh Criticism ◽  
The Right ◽  
Charging Stations

Under the challenge of climate change, fuel-based vehicles have been receiving increasingly harsh criticism. To promote the use of battery electric vehicles (BEVs) as an alternative, many researchers have studied the deployment of BEVs. This paper proposes a new method to choose locations for new BEV charging stations considering drivers’ perceived time cost and the existing infrastructure. We construct probability equations to estimate drivers’ demanding time for charging (and waiting to charge), use the Voronoi diagram to separate the study area (i.e., Shanghai) into service areas, and apply an optimization algorithm to deploy the charging stations in the right locations. The results show that (1) the probability of charging at public charging stations is 39.6%, indicating BEV drivers prefer to charge at home; (2) Shanghai’s central area and two airports have the busiest charging stations, but drivers’ time costs are relatively low; and (3) our optimization algorithm successfully located two new charging stations surrounding the central area, matching with our expectations. This study provides a time-efficient way to decide where to build new charging stations to improve the existing infrastructure.

scholarly journals Perceived Usage Potential of Fast-Charging Locations

2018 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Julia Krause ◽  
Stefan Ladwig ◽  
Lotte Saupp ◽  
Denis Horn ◽  
Alexander Schmidt ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Point Of View ◽  
Charging Infrastructure ◽  
Charging Time ◽  
Points Of Interest ◽  
Fast Charging ◽  
Business Perspective ◽  
Charging Stations ◽  
User Perspectives ◽  
Model Point

Fast-charging infrastructure with charging time of 20–30 min can help minimizing current perceived limitations of electric vehicles, especially considering the unbalanced and incomprehensive distribution of charging options combined with a long perceived charging time. Positioned on optimal location from user and business perspective, the technology is assumed to help increasing the usage of an electric vehicle (EV). Considering the user perspectives, current and potential EV users were interviewed in two different surveys about optimal fast-charging locations depending on travel purposes and relevant location criteria. The obtained results show that customers prefer to rather charge at origins and destinations than during the trip. For longer distances, charging locations on axes with attractive points of interest are also considered as optimal. From the business model point of view, fast-charging stations at destinations are controversial. The expensive infrastructure and the therefore needed large number of charging sessions are in conflict with the comparatively time consuming stay.

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