Public Charging Research Articles

Systematic Study of Battery Electric Vehicle Ownership in England

Battery electric vehicles (BEVs) form a big part of the UK’s agenda for decarbonizing road transport. Although there have been various policies such as purchase grants and vehicle tax exemptions, the BEV penetration rate in the UK was only 2.5% in September 2023. Most research analyzing electric vehicle (EV) ownership in the UK is either based on stated preference survey data or includes plug-in hybrid vehicles in the analysis because of the sparsity of BEV ownership data; there is limited research based on household revealed preference (RP) data. This paper develops a BEV ownership model using RP household-level data from England to discover influential factors, to validate the findings in the literature, or both. Specifically, this paper uses the subset of the UK National Travel Survey (NTS) special license data to estimate a series of binary logit models of BEV ownership as a function of several sociodemographic, regional, and temporal factors, and discusses the related policy implications. Household income, multivehicle ownership (resulting from range anxiety), and overnight parking on street (resulting from insufficient public charging infrastructure) are influential factors found in this study that align with previous studies. On the other hand, households with a mortgage loan, geographical attributes (such as population density), and household composition (e.g., number of adults and children) are new factors identified in this study. We also present a future BEV ownership prediction model for regions of England which clearly suggests that improving public charging infrastructure, especially in the north, is required to achieve widespread growth in BEV ownership.

Decision Support System for Determining the Location of Public Electric Charging Stations (SPLU) with Machine Learning

Electric vehicles are becoming increasingly popular as an environmentally friendly alternative, but the main challenge is the availability of adequate charging infrastructure. This research aims to develop a decision making system for determining the best public electric vehicle charging station (SPLU) locations utilizing machine learning. The purpose of this research is to find out the process of determining the location of Public Electric Charging Stations (SPLU) using a Machine Learning-based decision making system. This research uses quantitative methods with AHP techniques to determine the criteria weights and machine learning to recommend the best SPLU locations based on spatial data, surveys, and social media. The location recommendations are displayed in the form of an interactive digital map with visualizations of the suitability level to facilitate decision making.

Measuring the effectiveness of incorporating mobile charging services into urban electric vehicle charging network: An agent-based approach

Mobile charging service (MCS) has emerged as a cost-efficient solution to compensate for the limitations of fixed charging stations (FCSs) in service capacity and coverage. Nevertheless, the impacts of incorporating the MCS into the charging infrastructure network have not been well understood. Therefore, this study introduces an exhaustive framework for devising Agent-based Modeling (ABM) to delineate electric vehicle (EV) drivers' daily travels and charging behaviors within the integrated charging network. Specifically, the developed ABM framework encompasses both internal attributes of individuals and external variables of charging and transport networks that potentially shape EV drivers’ public charging decisions. It generates authentic daily charging demands for FCSs and MCSs and provides a streamlined process to further incorporate more charging modes into the EV charging ecosystem. Subsequently, we opted for the Salt Lake City metropolitan area to implement our framework and showcase the effectiveness of MCS integration on the charging network over various EV adoption levels and network settings. The findings of the designed case study unravel the choice patterns of EV drivers when facing diverse charging options and pinpoint the strengths and weaknesses of MCS incorporating into the urban charging network at different stages.

Journals Choice for Research Publication at Ethiopian Public Universities Academia in Decision Making Experience

The purpose of this study is to investigate how academic staff make decisions when choosing journals for publication at Ethiopian public universities. To realize this, a phenomenological research design was employed, and semi-structured interviews were conducted with 12 academic staff from six selected Ethiopian public universities using purposive sampling. Thematic analysis and narrative data analysis were used. The study found a preference for international journals over local ones and highlighted the influence of the Ministry of Education's policy. According to the study, 66% of the participants reported that their articles were rejected when they submitted them for publication in a journal. It also identified challenges such as a lack of awareness about distinguishing peer-reviewed journals from predatory ones, a lengthy review process, and insufficient funding for article publication charges. The study suggests that the Ministry of Education should support accredited local journals to gain recognition equal to highly indexed international journals, provide awareness training for academics about locally accredited and internationally indexed journals, and offer technical and financial assistance to local journals based on their performance to improve their acceptance by academics.

Electric truck feasibility for port drayage operations: Port Houston case study

Heavy-duty fleet electrification has the potential to reduce lifecycle operation costs, greenhouse gases, and criteria pollutant emissions. However, there are several obstacles to shifting from diesel fleets to full-electric fleets, such as range constraints and a lack of public charging infrastructure. This study developed an analytical feasibility framework for assessing the transition of an all-diesel heavy-duty fleet to a full-electric or a mixed electric and diesel fleet for drayage trucks operating at and around ports. The proposed framework devised a structured approach and multiple measures for determining the viability of heavy-duty fleet electrification, taking technological, operational, and economic aspects into account. The case study showed that, while battery electric trucks cannot replace entire diesel fleets yet, they are technologically and economically feasible for a portion of drayage operations. This practice will facilitate fleet electrification, particularly heavy-duty, by tackling major roadblocks and providing insight into the numerous unknown possibilities.

Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China

Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China

Public residential charging of electric vehicles: An exploration of UK user preferences

In the global transition from fossil-fuel to electric vehicles (EVs), drivers without a driveway or attached garage at home are most likely to be dependent upon public charging options if they adopt an EV. To better understand how such drivers can be supported to switch to EVs, this study assesses how public charging might best mimic key attributes of private residential EV charging by investigating the preferences of a sample of 2001 car drivers from households in the UK without a driveway or attached garage. A stated choice experiment offered on-street or car-park options for overnight, public residential charging (PRC) with seven attributes – charging fee, space guarantee, duration of stay, walk time, security measures, walk experience, and payment frequency. Logit and latent class modelling are used to identify the relative importance of those attributes, and the manner in which their valuation varies according to study participants’ experiential legacy. This legacy is to some extent informed by their social identity; residential environment; and parking conditions, perceptions and intentions. Walk experience is shown to be the most highly valued non-monetary attribute overall, not least because many women consider this a particularly important attribute of PRC options. Meanwhile, parking-related variables, such as whether participants currently park on-street or pay to park, influences both price-sensitivity, willingness to walk to PRC and the valuation of a space guarantee. We conclude that diverse public residential charging options should be designed to offer certainty, reliability and affordability in safe, attractive locations near households without parking on their private property.

Global electric vehicle charging station site evaluation and placement based on large-scale empirical data from Germany

Electromobility is a key technology to decarbonize transportation and thereby avoid the worst impacts of anthropogenic climate change. To power such vehicles when away from their home or depot, public charging infrastructure is required which can be split into enroute and destination charging. We define the latter as charging events that occur while users are busy with other activities. To fulfill this purpose, chargers need to be placed in locations where people spend time. This paper introduces a novel approach to do so based on a neural network trained on several thousand public charging stations in Germany. Within the training sample, the approach is able to predict how much energy was charged per station and day with an R2 of 0.61 for the training set and a RMSE of 13 kWh/day. Using the network, we predict utilization across urban, suburban and industrial areas in Europe and make those predictions available through an easy-to-use web interface. It is further possible to perform predictions and, thereby, extrapolate the learnings from Germany to any country with sufficient OpenStreetMap data. The introduced holistic methodology with its prediction and visualization phase is a first-of-its-kind by applying large-scale measured charging data to the placement problem while being usable in areas which have not yet rolled out electromobility.

Enabling EV Charging at varied locations using Smart Meters

Electric vehicles primarily run on stored electrical energy, which needs to be replenished at regular intervals of time. Commercially, EVs are charged at charging stations, however they can be charged anywhere whether it be at home, public charging stations or even at workplaces. The commercial charging stations are highly costly and require huge setup. The main aim of this paper is to enable charging at any commercial and non-commercial establishment and optimize the cost of charging and grid usage by using an IOT (Internet of Things) device for bill generation and grid usage analysis.The charge enabling process is based on a QR based activation of the device. The user scans the QR code and registers his details, this enables the charge cycle. The bill is generated based on the start time and the end time of the charge cycle and the energy consumed in that cycle and a text message is sent to the consumer. The effect of charging load is also analyzed and hence an appropriate action is taken to optimize the peak load or demand. Keywords: Internet of Things (IoT), Electric Vehicles (EVs), Smart Energymeter, QR-based activation

Evaluating planning and operational policies for urban public electric vehicle charging infrastructure

It is expected that public electric vehicle charging demands will gradually increase in Korea. In this study, we propose a macroscopic indicator of the level of service (LOS) of urban public fast charging infrastructure to quantitively evaluate the joint impacts of possible planning and operational policies on it. The indicator is defined as the reliability-based capacity, the number of electric vehicles that can be charged within the maximum targeted weighted delay between wait and detour. We consider three combinable policies: changing the distribution of chargers in the planning stage; assigning the optimal charging station to each charging event in real-time and evenly distributing daily charging demands in the operational phase. For multiple policy combinations and various demand cases, we compare their LOS levels, calculated by Monte Carlo simulations that can efficiently address the complex joint impact of stochastic demands. The simulation experiments are designed to explicitly discover the impacts of policies along with varying charging demands. The results show that certain combinations of proposed policies can effectively and efficiently improve LOS.

Navigating the Cardiothoracic Publication Landscape: A Primer for Journal Selection.

Journal selection is very important for any researcher in every field. Publication in a reputable journal not only has a higher reach among a greater number of people but also sets a benchmark for significance and quality. This primer aims to guide researchers in the field of cardiothoracic research, including medicine, surgery, and imaging, to assist in journal selection for their respective articles. Journal selection depends on a variety of factors, such as impact factors, publication charges, review processes, article types that a journal accepts, and indexing. This primer highlights all these factors in detail that are essential for the selection of a suitable journal. The article emphasizes the importance of these factors in making a reasoned decision about journal selection. This article also focuses on different types of publication models and their implications, including hybrid open access, gold open access, and diamond open access. In conclusion, this primer aims to provide insights to researchers in the fields of cardiothoracic surgery, medicine, and imaging about the publication landscapes and guide them to strategically plan their submissions.

A two-sided equilibrium model of Vehicle-to-Vehicle charging platform

A novel mobile charging service utilizing vehicle-to-vehicle (V2V) charging technology has been proposed as a complement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study develops a two-sided equilibrium model for a V2V charging platform, where the demand-side charging vehicles (CVs) choose between charging piles and the V2V platform, and the supple-side discharging vehicles (DVs) decide whether to provide discharging services. The V2V platform matches CVs with DVs, receiving compensation from CV owners and reimbursing DV owners. To explore the strategic interactions between the V2V platform and fixed CI operators, we construct an analytical bi-level model that examines different operation regimes, including competitive and cooperative scenarios, i.e., non-cooperative game and Nash Bargaining game. The lower-level model is a two-sided equilibrium model that quantifies charging and discharging choices of EV owners, while the upper-level model optimizes the pricing decisions of the V2V platform and fixed CI operator. Numerical results indicate that the introduction of the V2V platform can reduce the overall charging costs, thus encourage the use of public charging services. Furthermore, it is suggested that the V2V platform be managed by independent enterprises, rather than existing fixed CI operators, to promote the EVs at relatively low charging costs.

UNDERSTANDING THE ROLE OF V2G TECHNOLOGY ON GRID STABILITY DURING PEAK HOURS IN INDIA

India has set an ambitious goal of becoming Net Zero in carbon emissions by 2070. To achieve this goal there are significant steps being taken especially in the power and electric vehicle sector. A revolution that involves symbiosis of both the sectors, is the integration of V2G (Vehicle to Grid) technology in India. This paper gives an overview of power sector and electric vehicles market in India followed by need, benefits, and infrastructure required for bringing V2G in India. It also includes theinsights froman interview with the founder of SHERU, a start-up which provides cloud energy storage and is working closely with the related concepts. The methodology to calculate crude oil saving, C02 emission, journey cost, public charging and home charging is also discussed. The paper looks into the case studies of few developed countries that have been using V2G and emphasizes on how this technology can help to achieve sustainability goals.

Design of Coin Sensor based Mobile Charging System

Abstract—Today, mobile phones play an important role not only in the modern world of communication, but also in our daily lives. The mobile phone business is currently worth billions of dollars and supports most features of all mobile phones with different operating systems. Therefore, running these phones requires a public charge, which should be useful to the general public. Hence, keeping mobile phones charged has become a more significant task. In this project, we designed a prototype for mobile battery charging on coin insertion. A coin-based mobile charging system provides an alternate solution to all mobile users for charging their mobile phones during travelling or emergency where they may not have access to conventional power banks. This system can be used by shop owners, the general public and can be implemented in public places like railway stations and bus stand to provide mobile charging facilities. This system can also be implemented in commercial complexes, colleges, and offices. This coin based mobile charging system charges the mobile phone when the valid coin is inserted. So the coin acceptor used recognizes the coin inserted and signals the Arduino Uno for further action. When a coin is inserted, it signals the Arduino Uno and starts charging the mobile phone providing a 5V power supply through the charging slot. The Arduino Uno starts a reverse countdown timer to display the charging time . left for that mobile phone using LCD display. Further the user adds another coin, the Arduino Uno adds to the currently remaining time and once again decrements the countdown. Keywords: Arduino Uno, coin-based mobile charging system, LCD display, mobile phone. Index Terms—Arduino Uno, coin-based mobile charging system, LCD display, mobile phone.

The development of consumer preferences for electric vehicle charging infrastructure in China: Evidence from a questionnaire survey with a four-year interval

Consumer preferences are important in guiding the development of electric vehicle charging infrastructure. Chen and Lin (2022) examined the factors influencing consumer preference for charging infrastructure (CPCI) with data from 2019. Four years later, the research team conducted a new questionnaire survey for comparative analysis. Using a total of 3,778 questionnaires, the authors explored the differences in consumer traits and CPCI at various periods and regions. The potential role of CPCI in promoting electric vehicles was also further analyzed. The main findings include (1) Consumers’ charging habits have changed in the last four years, favoring the use of private charging infrastructure and nighttime charging. (2) Consumers in first-tier cities prefer to use public charging infrastructure than those in non-first-tier cities. (3) Owning private charging infrastructures contributes to consumer satisfaction, which in turn promotes the re-purchase and recommendation of electric vehicles. Preferences for private charging infrastructure strengthen this effect. Finally, the authors provide targeted policy recommendations.

Decarbonizing transportation: A data-driven examination of ICE vehicle to EV transition

Transportation is one of the sectors with the highest CO2 emissions, accounting for 23% globally and significantly contributing to climate change. To address this challenge, the authorities have proposed new stringent policies that lead to decarbonization. From this perspective, this work proposes a multi-scenario analysis for the electrification of a fleet of private users. The scenarios differ on the type of charging mode adopted: slow charging (charging modes 1 and 2) and fast charging (charging modes 3 and 4). The model aims to identify the percentage of potential users who can shift from Internal Combustion Engine (ICE) to Electric Vehicles (EVs) in different scenarios. Furthermore, the model will highlight the average expenditure of users for charging, highlighting how the cost of energy could be a driver for the electrification of the sector. Finally, the model will allow us to evaluate the savings of up to 220 tons of CO2/year thanks to the electrification of the sector with Long Range vehicles, in best case scenario. The use of a multi-scenario analysis allowed several possible electrification solutions to be explored, highlighting the strengths and weaknesses of the charging mode used, supported by quantitative results. This data-driven approach allows us to identify optimal locations for public charging stations in region of northern Italy region, where the data was sourced, which will help to encourage the switch to EVs.

An Analytical Framework for Assessing Equity of Access to Public Electric Vehicle Charging Stations: The Case of Shanghai

With the unprecedented growth of electric vehicles usage, the equitable population-based provision of public charging services has become an important concern in high-density urban centers. To address sustainability concerns, this study explores an analytical framework for assessing the equity of access to public charging services. By comprehensively analyzing factors such as accessibility, the Gini coefficient, the correlation coefficient, and supply–demand matching, we investigated the unequal access to public charging stations within 24 types of sites in central Shanghai. The spatial distribution and accessibility were visualized to illustrate differences in service access. Subsequently, social equity was assessed by considering the population distribution and identifying areas of supply–demand imbalance. The results show that 81% of households share only 10% of public charging services, suggesting a generalized inequality within areas and facilities. Residents of large-scale, low-density, low-grade neighborhoods have difficulties accessing services. Nearly 66.96% of subdistricts have supply and demand conflicts. In addition, priority types of improvement were identified and directions for improvement were suggested, as well as recommendations for the integration of PCSs with exterior built places. We also found significant differences in accessibility and equity at both the district and subdistrict level. The findings of this study will help urban planners assess and locate unequal areas and provide insights and the basis for further expansion into the analysis methods adopted at different stages to achieve sustainable development.

Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas emissions is hindered by the lack of public charging infrastructure, particularly fast-charging stations. Given that electric vehicle fast charging stations (EVFCS) can burden the electricity grid, it is crucial for EVFCS to adopt sustainable energy supply methods while accommodating the growing demands of EVs. Despite recent research efforts to optimize the placement of renewable-powered EV charging stations, current planning methods face challenges when applied to a complex city scale and integrating with renewable energy resources. This study thus introduces a robust decision-making model for optimal EVFCS placement planning integrated with solar power supply in a large and complex urban environment (e.g., Chicago), utilizing an advantage actor-critic (A2C) deep reinforcement learning (DRL) approach. The model balances traffic demand with energy supply, strategically placing charging stations in areas with high traffic density and solar potential. As a result, the model is used to optimally place 1,000 charging stations with a random starting search approach, achieving total reward values of 74.30 %, and estimated the capacities of potential EVFCS. This study can inform the identification of suitable locations to advance the microgrid-based charging infrastructure systems in large urban environments.

The Impact of Consumer Sentiment on Sales of New Energy Vehicles: Evidence from Textual Analysis

The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment analysis of textual reviews, used word frequency statistics to explore consumers’ views on the attributes of new energy vehicles, and constructed a consumer sentiment index to study the impact of consumer sentiment on NEV sales. Considering the dependence of NEVs on a charging station, this paper explores the nonlinear impact of the popularity of charging stations on the relationship between consumer sentiment and sales of new energy vehicles. The findings indicate the potential for enhancement in the areas of space, interior design, and comfort of NEVs. Additionally, consumer sentiment was found to facilitate the diffusion of NEVs, with this effect being heterogeneous across different educational backgrounds, income levels, and ages. Furthermore, the availability of per capita public charging stations was shown to significantly reduce range anxiety and encourage consumer purchasing behavior.

Toward just and equitable mobility: Socioeconomic and perceptual barriers for electric vehicles and charging infrastructure in the United States

Based on a large-scale public survey, we identify and quantify the significance of key factors associated with the deployment of electric vehicles and charging infrastructure. Our results indicate that individual characteristics, such as income, age, region, and single vs. multi-family housing type can significantly affect electric vehicle purchase preferences, especially those concerning overnight charging and perceptions of benefits and barriers. Moreover, our results challenge earlier findings in the literature by showing how certain elements, such as expected electric driving range, certain travel behaviors (e.g., driving distance, destination types), the most common perceived benefits (e.g., cleaner air) or barriers (e.g., reliability concerns), and preferred location for public charging seem to not vary much or at all with the socioeconomic, demographic, and geographical variables examined in this study. We conclude with the implications for policies to advance equitable vehicle electrification. Our findings underscore the importance of lower-cost models of electric vehicles, home and public charging access, charging infrastructure planning, more integrated analysis of interlinked housing and transportation needs and solutions, the availability of alternative transportation modes, and the potential role of gas stations for electric vehicles. We encourage others to build on these results and have shared our complete survey instrument as an added contribution.