Spatiotemporal Analysis of the e-Mobility System in Newcastle-Gateshead Area (original) (raw)
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In this paper we report on research into patterns of electric vehicle (EV) commuters' movement and behavior in a road network. The design of the charging network is a function of its size and the distribution of the charging points within a given urban area. It consists of several spatial design qualities, configuration attributes, travel demand, and users charging patterns. In order to have reliable recharging facilities (RFs), we need to understand the nature of the system and eventually plan and design for the current and potential EV commuters. The recharging experience should not be a worrying matter for EV drivers. Assessing existing systems helps in picking up the main paradigms of EV population and system that need to be considered or even adjusted for a better EV market penetration. This study introduces the spatial configuration of an active e-mobility system through a case study. The paper investigates the correlation between the design characteristics of EV recharging infrastructure and its usability for the given metropolitan area. The usability is a consequential communication corresponds to the system design. We need to explore the variations in individual charging behavior within the EV population to understand the movement patterns in the network. Using data of over 500 EV drivers charging their cars using public charging infrastructure over a three-year time, we clustered the EV population based on the charging patterns. Design configuration analysis is conducted using DepthMap; charging patterns are captured by the infrastructure service provider while been sorted, tabulated and analyzed using SPSS. The study outcomes should give a clear insight of how the use of RFs is affected by the spatial; design features as well as the charging patterns and profiles of EV real users.
King Fahd University of Petroleum and Minerals, 2023
Planning for the improvement of existing infrastructure is another component of electric cars. This calls for spatial modeling techniques that incorporate information on potential electric car owners and the locations of likely charging stations. The adoption of such cars depends significantly on lifestyle, age group, and socioeconomic conditions, which impact where to locate charging station sites. Given demographic patterns on who is most likely to acquire electric cars, the use of census data inside spatial regression models becomes beneficial. Particle swarm optimization is a different approach that incorporates traffic patterns, building costs, infrastructure expenses, and power availability. GIS is becoming increasingly important in developing infrastructure for electric vehicles to make them more accessible to more individuals who commute by automobile. For one thing, unlike conventional cars, planning the shortest or fastest route to a destination may not be possible for an electric vehicle since it may need to recharge along the way. Powering stations, especially those that are available and charge relatively rapidly, should often be considered in the computation. Because of this, it is crucial to employ real-time data on specific power points, traffic patterns, and road conditions. Some free information on charging stations that aid in navigation is offered by ArcGIS.
Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context
IEEE Access
The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand by loading local power grids, but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that exploits predictors compiled from Geographic Information Systems data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators that measure the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l 1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies. INDEX TERMS Electric vehicles, data analysis, charging infrastructure, spatial analysis, prediction methods, machine learning.
Procedia Manufacturing, 2015
Today's cities endure major risks like global warming due to gas emissions. This is challenging for both humans and technical developments. E-mobility, however, raises the opportunities of enhancing a more sustainable mobility (structure) and providing healthier living in future cities. The paper identifies not only which locations of fast-charging stations in a city are preferred but also reveals how users evaluate them. With multiple empirical focus group studies, a user-centered evaluation could be carried out. This is important to identify possible trade-offs for infrastructural planning due to e-mobility use in the future. In total, eight main evaluation criteria for fast-charging locations regarding both position and concrete realization were derived in the subsequent discussions about the charging infrastructure. An overall accordance could be identified in the discussions, which indicates a step in the right direction for the long term goal of building a charging network with the help of user preferences.
Electric Vehicle Charging Stations Coverage: A Study of Slovenia
Tehnicki vjesnik - Technical Gazette, 2022
To promote the penetration of electric vehicles (EVs), it is of great importance to plan and construct charging stations rationally. In this sense, the state of Slovenia's charging station coverage was analysed. Using discrete and network geographic information system (GIS) models, with ArcGIS software, the density of electric vehicle charging stations (EVCSs), geographic distribution, nearness along a street network, and clustering analyses were performed. A survey conducted among Slovenian users of EVs supported the GIS analysis. It was found out that the distribution of EVCSs has an east-northeast to west-southwest directional trend. Only 13% of EVCSs are accessible from the nearest motorway at a distance of 500 meters or less. An insight into intrinsic clustering structure revealed 11 clusters of EVCSs from which the most distinct is the cluster on the area of Ljubljana. The scientific contribution of the research is in the integration of GIS, spatial analysis and the results of a survey to study the coverage of EVCSs in a certain region. Spatial analyses are carefully selected, and, in complementarity, give a comprehensive picture of EVCSs coverage. The research is important for further spatial planning of EVCSs.
Predicting popularity of EV charging infrastructure from GIS data
ArXiv, 2019
The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand, loading local power grids but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that is exploiting predictors compiled from GIS data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators measuring the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l1 penal...
Development of an assessment model for predicting public electric vehicle charging stations
European Transport Research Review, 2018
Deploying an adequate electric vehicle (EV) charging infrastructure to support the increasing EV market is one of the major strategic goals of the U.S. government. This requires a well-designed EV charging network. The distribution and capability of the existing charging networks in terms of EV population, location, charging rate, and time of charging in San Diego is examined. A mathematical model to calculate the demand number of public Level 2 chargers universally applicable is developed. The study showed that although San Diego has sufficient chargers to accommodate the existing EV's charging demand, the current public charging distribution network is neither well designed nor effectively used. To eliminate the waste resulting from the inefficiently designed charging infrastructure and maximize the usage rate of each charger, it is recommended that the designed optimal model to be utilized and the charging location priority be implemented to improve the availability and accessibility of charging network in the City of San Diego. Introduction: The purpose of this study is to identify current problems with the existing electric vehicle public charging stations and come up with solutions to improve the availability and accessibility of public charging stations in the City of San Diego. The objective of this research project is also to develop a mathematical model to predict the demand of EV chargers in any city including in the City of San Diego. Methods: A mix of quantitative and qualitative research methods are used to analyze the problem. The first phase of this project is to determine the study area by identifying the existing problems and issues from existing sources, and formulating hypothesis. Results: The distribution and capability of the existing charging networks in terms of EV population, location, charging rate, and time of charging in San Diego was examined. A mathematical model to calculate the demand number of public Level 2 chargers for the City of San Diego and for each zip code was developed. Among 361 tested public Level 2 chargers distributed in 34 communities, 66 chargers located at 37 charging stations distributed in 22 communities were found to be nonoperational or damaged but still operational. They accounted for 18% of the total number of tested EV charging stations and 12.7% of the total public Level 2 in San Diego. The model tested using data from San Francisco Bay Area, and Los Angeles County matched well to the predictions.
Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis
Energies, 2018
Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals hig...
Applied Sciences, 2019
This work investigates minimum charging infrastructure size and cost for two typical EU urban areas and given passenger car electric vehicle (EV) fleets. Published forecasts sources were analyzed and compared with actual EU renewal fleet rate, deriving realistic EV growth figures. An analytical model, accounting for battery electric vehicle-plug-in hybrid electric vehicle (BEV-PHEV) fleets and publicly accessible and private residential charging stations (CS) were developed, with a novel data sorting method and EV fleet forecasts. Through a discrete-time Markov chain, the average daily distribution of charging events and related energy demand were estimated. The model was applied to simulated Florence and Bruxelles scenarios between 2020 and 2030, with a 1-year timestep resolution and a multiple scenario approach. EV fleet at 2030 ranged from 2.3% to 17.8% of total fleet for Florence, 4.6% to 16.5% for Bruxelles. Up to 2053 CS could be deployed in Florence and 5537 CS in Bruxelles, ...