Public Charging Infrastructure Research Articles

Battery Electric Vehicles: Travel Characteristics of Early Adopters

Do U.S. households with battery electric vehicles (BEVs) drive less or more than U.S. households with internal combustion engine vehicles (ICEVs)? Answering this question is important to policymakers and transportation planners concerned with reducing vehicle miles traveled and the emissions of greenhouse gases from transportation. So far, this question has not been answered satisfactorily, possibly because of the relatively low number of EVs in the U.S. until recently, but also because of methodological issues. In this paper, we aim to fill this gap by analyzing data from the 2017 National Household Travel Survey (NHTS). We apply propensity score matching (PSM), a quasi-experimental method, to examine the differences in self-reported annual mileage and calculated daily mileage for various trip purposes among households with only BEVs (BEV-only), households with both BEVs and ICEVs (BEV+), and households without BEVs (non-BEV households). Our findings indicate that households with BEVs drive fewer annual miles than non-BEV households, but typically travel no less than they do for daily activities. This apparent discrepancy is likely due to taking fewer longer trips because the public charging infrastructure was still in its infancy in 2017, and its reliability was questionable. As technological progress is helping to overcome current battery limitations, policymakers may consider measures for fostering fast charging technologies while pondering new measures to fund both the charging infrastructure and the road network.

What factors affect the development of public charging infrastructures? a study from the perspective of potential users

The rapid development of the electric vehicle market has greatly stimulated the demand for public charging infrastructure (PCI) and made it a pertinent topic to improve its charging service quality in the industry. Data from perceived preference, PCI and electric vehicles cannot reflect the factors influencing potential users’ anxiety. This study designed a PCI development evaluation framework to investigate the impact of economic, technological, market, policy, and social environmental factors on PCI development from the perspective of potential users. We collected a dataset including 386 potential users in Tianjin, China, and employed structural equation model to survey the implementation of PCI. The following conclusions were drawn from this study: 1) Reducing operating costs will not significantly increase PCI charging user demand and improve use efficiency for potential users; 2) Technical factors cannot directly promote the development of PCI but will reverse lag the development of the market; 3) The growing market demand is the main impetus to the development of electric vehicle industry, and the incentive policy and social environment can indirectly incentivize PCI development. The policy implications suggest that the PCI industry can experience sustainable development by continuously innovating market-oriented business models, and improving policy systems and industry mechanisms. This study provides analytical foundation and decision support for policymakers and pertinent industry participants, promotes the development of electric vehicle-related industries, and helps achieve the strategic goal of carbon neutrality.

Understanding the opportunity-centric accessibility for public charging infrastructure

In this study, we utilize data from over 28,000 public charging stations (PCSs) and 5.5 million points of interest across twenty U.S. metropolitan areas to underscore the importance of considering the availability of daily activity opportunities when assessing accessibility to PCSs, rather than relying solely on spatial proximity. Specifically, we conduct comprehensive comparisons of opportunity-centric accessibility measures with distance-based measures and perform counterfactual analyses under various PCS deployment strategies. Our findings reveal significant inequalities in PCS access across different neighborhoods under distance-based and opportunity-centric measures. However, a greater disparity exists when considering opportunities, with high-income communities having significantly better access to PCSs. Counterfactual analyses suggest that equitable deployment based on distance measures results in the least equitable outcomes when considering opportunities, primarily due to the existing disparity in opportunity distributions in our cities. Our insights highlight the complexity of locating PCSs and can guide nationwide PCS deployment for long-term societal benefits.

Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energy-efficient, low-noise, and can intelligently interact with smart grids through Vehicle-to-Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.

How Many More Public Charging Stations Do We Need? A Data-Driven Approach Considering Charging Station Overflow Dynamics

The development of public charging infrastructure is crucial to support mass electric vehicle (EV) adoption. Although many cities worldwide have already installed an initial network of public chargers, it is often unclear whether the current supply of infrastructure is in line with demand and how many more charging stations are required to cope with future EV growth. In this sense, transactional charging data on the existing network can help answer these questions. We present a novel method that uses historical charging data as input to obtain answers to the following questions: (a) How many more chargers are required to meet future demand? and (b) Where should these new chargers be installed? By mining the individual charging behavior of EV drivers, we show that overflow dynamics can be found between charging stations. That is, when a preferred charging station is fully occupied, it is found that EV drivers divert to other charging stations nearby. Identifying these dynamics allows us to simulate the impact of a demand increase on the charging infrastructure network more accurately. We found the number of new chargers required to be significantly lower when considering overflow dynamics. Our simulations indicate that if demand is doubled, 30%–50% fewer charging points are needed compared with a situation in which overflow dynamics are neglected but the same failure rate is still maintained (i.e., percentage of failed charging sessions in the network). Determining the exact number of chargers will depend on the failure rate policymakers are willing to accept, reflecting the trade-off between charging convenience and utilization.

Bill It Right: Evaluating Public Charging Station Usage Behavior under the Presence of Different Pricing Policies

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.

Electric vehicles adoption challenges in Oman: a comprehensive assessment and future prospects for sustainable cities

The rising popularity of Electric Vehicles (EVs), fuelled by technological advancements and supportive government policies, presents a promising solution to reduce carbon emissions from conventional cars. This study explores public perceptions of obstacles to Electric Vehicle (EV) adoption in Oman and assesses EVs' impact on city sustainability. Over 300 car consumers in Muscat participated in a survey, facilitated through Microsoft Forms, and distributed via email and social media platforms like WhatsApp, Facebook, LinkedIn, and Instagram. Data analysis utilized descriptive statistics and Structural Equation Modelling (SEM). The study finds that the main obstacles to EV adoption in Oman include a lack of public charging infrastructure, limited EV knowledge, and concerns about available EV models. Safety issues, financial constraints, and low public awareness of climate change also hinder adoption. Structural SEM results show positive correlations between cost-effectiveness, charging infrastructure availability, climate change awareness, and intention to adopt EVs. However, safety concerns negatively impact EVs purchase intention. Regarding the impact of EVs on Oman's cities, respondents generally believe in the potential for EVs to improve air quality, enhance urban living conditions, and mitigate noise and air pollution. However, the respondents were also cautious about the widespread use of EVs due to the high infrastructure investment costs and energy consumption. Therefore, this study recommends targeted interventions like awareness campaigns, better charging infrastructure, and adoption incentives. These insights are vital for policymakers and stakeholders aiming to promote sustainable EV adoption in Oman and beyond.

Utilizing electric bus depots for public Charging: Operation strategies and benefit analysis

The uneven distribution of public charging infrastructure poses significant challenges for private electric vehicles (EVs). Many Chinese city bus depots have abundant chargers for electric buses (EBs), often unused during daytime operations. This study proposes an innovative mechanism to utilize bus depot chargers for private EVs, prioritizing the charging demands of EBs. Uncertainty in EB and EV charging demand, EV arrival time are considered. The objective is to maximize bus company revenue through a two-stage stochastic model. Practical applicability is demonstrated through a real-world bus depot case in Shanghai with 127 EBs and 376 private EVs. The results show that allocating 58% of the charging power and 30% of the chargers to the public from 10:00 to 24:00 meets 97.54% of the private EVs’ charging demand. The bus operator faced a charging cost of 10,747 CNY, while now it expects a profit of 1733 CNY with the project running.

Suitable law-based location selection of high-power electric vehicles charging stations on the TEN-T core network for sustainability: a case of Poland

With the upcoming implementation of the amendment to Regulation (EU) 2019/631 of the European Parliament and of the Council, from 2035 there will be a ban on the registration of new vehicles with internal combustion engines (ICE) in the Member States of the European Union (EU). Consequently, changes in the transportation sector, resulting from the increasing use of electric vehicles, appear to be inevitable. According to the adopted legal acts, the European Union Member States will be obliged to develop, among others, a charging infrastructure and access to public charging stations for electric vehicles. As a result, there will be a need to ensure a significant increase in the power and the number of charging stations and to determine their appropriate location. The article presents the challenges faced by charging station operators and difficulties related to the further development of electric vehicle charging infrastructure in Poland. The still poorly developed public charging infrastructure for electric vehicles, especially in service areas located along the main communication routes, remains the main obstacle to the development of electromobility. In the context of legal, financial, technological, and organizational challenges, the problem of the proper distribution of electric vehicle charging stations along the main communication routes is therefore of particular importance. The aim of the article is to present a new, proprietary method for determining the location of electric vehicle charging stations in Poland within the Trans-European Transport Network (TEN-T), which considers objective location factors: adherence to AFIR requirements, the specificity of the Polish power system and existing parking infrastructure. As a result of using the developed method, a list of 188 recommended locations for the construction of electric vehicle charging stations in Poland along the Trans-European Transport Network (TEN-T) was created. It has been shown in this way that the use of the presented method enables the suitable determination of the location of electric vehicle charging stations along transport routes, considering legal, financial, and technological requirements, which will significantly facilitate the operation of zero-emission transport.

Policy analysis for sustainable EV charging facility adoption using SEM-ANN approach

Researchers often reported that the non-availability of public charging infrastructure is the primary concern in adopting electric vehicles (EVs) in India. Adopting a home charging facility would help at the preliminary stage of EV introduction, where there is less scope for public charging infrastructure. In addition, integrating the solar rooftop photovoltaic (PV) facility to generate electricity for EV charging would help to develop a sustainable change in the transport sector. Thus, this study aims to identify various influencing factors that affect individual householder's decisions to adopt a home charging facility supported by a rooftop PV system for EV charging. Also, this study identifies and prioritises the most suitable policy options to encourage users to adopt EVs, including home EV charging supported by rooftop PV facilities. For this purpose, a questionnaire survey was conducted with individual householders in Ahmedabad, India, and 850 samples were used for the analysis. An integrated partial least squares-structural equation modelling with the artificial neural network method was adopted in this study. The study's findings reveal that the comfort and convenience of charging a vehicle at home, reduced range anxiety, comparatively lesser charging cost, flexibility to charge the vehicle, and no waiting to charge the vehicle are significant motivating factors for adopting a home charging facility. The willingness to adopt a rooftop PV system positively influences EV charging at home. In addition, providing a combined incentive to install home EV charging and a rooftop PV facility is the most preferred policy option. The outcome of this study would significantly help transport planners, policymakers, and researchers develop effective strategies and policies to boost EV sales in the Indian context.

Analysis of Spatiotemporal Characteristics and Influencing Factors of Electric Vehicle Charging Based on Multisource Data

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.

Connecting the Dots: A Comprehensive Modeling and Evaluation Approach to Assess the Performance and Robustness of Charging Networks for Battery Electric Trucks and Its Application to Germany

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks’ performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks’ performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks.

A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

This paper presents a novel and practical data-driven approach to sub-optimally allocate charging stations for electric vehicles (EVs) in an early-stage setting. Specifically, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed in order to promote the transition of EVs from traditional cars? We develop a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -nearest model and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest model that can capture people's satisfaction towards a certain design and formulate the early-stage EV charging station placement problem as a monotone submodular maximization problem utilizing fine-grained population, trip, transportation network and POI data. A greedy-based algorithm is proposed to solve the problem efficiently with a provable approximation ratio. A case study of Haikou is provided to demonstrate the effectiveness of our approach.

Strategic investment in electric vehicle charging service: Fast charging or battery swapping

With the increasing adoption of electric vehicles (EVs), there is a growing need for public charging infrastructure. As a result, significant investments have been made in charging services, particularly, fast-charging (FC) and battery-swapping (BS) services. This paper examines the impact of technical and operational factors, as well as market conditions, on the pricing and profitability of each service to explore whether and how EV charging service providers should invest in these emerging charging services. The analysis with benchmark to private-use slow-charging (SC) services reveals that if the valley electricity price is high and the potential market size is small, lowering service costs does not make BS services a viable option. When the valley electricity price is low, reducing battery loss will not give FC services an advantage. However, in such scenarios, BS services can gain an edge by decreasing service costs. Interestingly, even if both SC and BS services are negatively affected by higher valley electricity prices, the impact on the profitability of BS services is more severe. Our results provide implications for the development of public EV charging service infrastructure. We recommend that implementing energy storage solutions can help alleviate the negative consequences of escalating valley electricity prices and wider peak–valley electricity price differences on BS services and FC services, respectively.

An analytical framework for assessing equitable access to public electric vehicle chargers

Inequitable distribution of public electric vehicle (EV) charging facilitiesmay result in disparities in charging accessibility, potentially impeding the uptake of EVs. In response, this paper proposes an analytical framework to assess the accessibility-based equity of public EV charging infrastructure systematically, using Hong Kong (HK) as the study area. The results demonstrated severe spatial inequity of charging facilities in central, north, and southwest HK. Meanwhile, the Gini index of all public EV charger types for the total population was 0.751, and the indices for low-income, low-education level, and government-funded housing subgroups were 0.791, 0.809, and 0.893, respectively. These indicated considerable horizontal and vertical inequity from a statistical perspective. Furthermore, age, education level, family structure, and housing type were identified as significant socio-demographic characteristics correlated to the accessibility-based equity of public EV charging infrastructure in HK. These findings are expected to be useful for future policymaking and infrastructure planning.

How the presence of public charging infrastructure can impact the adoption rate of electric vehicles in the UK

In the current work, the presence of public charging facilities and their impact on the adoption rate of Electric Vehicle (EV) are studied across 363 Local Authorities (LA) districts in the UK. This analysis is conducted utilizing the data from the UK government on the location of public electric vehicle chargepoints in various LAs as well as the EV ownership rate between years 2011 and 2021. Various rural, urban and extra urban (e.g. London) regions are analyzed to assess whether EV ownership has been impacted as the charging infrastructure is rolled out in these regions. The quarterly data on the number of licensed Ultra Low Emission Vehicles (ULEV) in each UK local authority is used to track EV ownership levels. The results indicate that the publicly available charging points stimulate the diffusion of EV adoption; however, this varies by region and saturation. The reverse effect of EV ownership on charging infrastructure rollout by Local Authorities cannot be entirely rejected; however, the positive stimulus of public infrastructure on adoption of EV is established. The findings can provide the UK policy makers with insight into the public charging infrastructure requirements and benefits in short to medium term to meet the committed emission reduction targets by 2035 and 2050.

A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure

As Electric vehicle (EV) adoption increases worldwide, public charging infrastructure must be expanded to meet the growing charging demand. Furthermore, insufficient and improperly deployed public charging infrastructure poses a real risk of slowing EV adoption. The infrastructure thus needs to be expanded intelligently and flexibly while accounting for uncertain dynamics in future charging demand. Nevertheless, current methods for demand-based expansion often rely on rigid and error-prone tools, such as travel surveys and simple rules of thumb. The former is more appropriate for long-term, equilibrium scenarios, where we consider the charging network as a whole rather than incrementally expanding it. At the same time, the latter relies on business experience in a rapidly changing field. We propose a predictive optimization approach for intelligent incremental expansion of charging infrastructure. At each time step, we estimate the future charging demand through a Gaussian Process, which is subsequently used in a linear chance-constrained optimization method to expand the charging infrastructure incrementally. To develop and validate this framework, we account for environmental feedback by simulating user behavior changes based on historical charging records and considering an optimized charging network at every iteration. We apply this approach to a case study of EV charging in Dundee, Scotland. We compare different strategies and reasons for their pros and cons for monthly incremental expansion of the charging network. In particular, combining machine learning and optimization results in the cheapest expansion and one that serves the most demand.

Deployment priority of public charging speeds for increasing battery electric vehicle usability

To inform charger deployment decisions, this paper aims to understand the potential utilization and deployment priority for public charging infrastructure. A data-driven Cumulative Public Recharging (CPR) model is developed to explore the travel patterns by using 2017 National Household Travel Survey data. Given the daily trip sequence, trip distance, and dwell times, the study examines the daily expected driving range and BEV feasibility under different charging speeds, battery capacity, and charging behaviors. The results suggest that more advanced public chargers increase the daily expected driving range. Home charging is sufficient for most daily short-distance trips while public chargers are still needed for medium- and long-distance trips. Extreme fast charging (xFC) may not be necessary for people with home charging but could be more useful for people without home charging and for urgent charging. xFC becomes even less important to drivers with longer BEV ranges, a finding that contradicts conventional thinking.

Car choice determinants in Italy and Norway: A comparison based on revealed and stated choices

Norway is the leading country in electric car adoption in the world, while in Italy electric cars are only recently gaining acceptance. We compared car choices in the two countries highlighting commonalities and differences in the choice determinants, distinguishing between the small and the large car segment. We analyzed actual choices made in the real-world conditions and stated choices under hypothetical scenarios. The comparison between the preference structures of the two countries shows important differences when the revealed preference dataset is analyzed, while the differences are much reduced with the stated preference dataset. All in all, we feel that the two countries present only differences associated with longer car driving habits of the Norwegian drivers, the higher percentage of large cars in Norway, and the more developed public charging infrastructure. Since the supply of cars is quite similar, such a consideration leads us to believe that the huge discrepancy in electric car uptake is mainly due to the different car policies adopted in the two countries. The evolution of the policy setting and of the technology will determine whether Italy will follow the Norwegian model of gradual BEV uptake.

Planning of fast charging infrastructure for electric vehicles in a distribution system and prediction of dynamic price

The increasing number of electric vehicles (EVs) has led to the need for installing public electric vehicle charging stations (EVCS) to facilitate ease of use and to support users who do not have the option of residential charging. The public electric vehicle charging infrastructures (EVCIs) must be equipped with a good number of EVCSs, with fast charging capability, to accommodate the EV traffic demand, which would otherwise lead to congestion at the charging stations. The location of these fast-charging infrastructures significantly impacts the distribution system (DS). We propose the optimal placement of fast-charging EVCIs at different locations in the distribution system, using multi-objective particle swarm optimization (MOPSO), so that the power loss and voltage deviations are kept at a minimum. Time-series analysis of the DS and EV load variations are performed using MATLAB and OpenDSS. We further analyze the cost benefits of the EVCIs under real-time pricing conditions and employ an autoregressive integrated moving average (ARIMA) model to predict the dynamic price. The simulated test system without any EVCI has a power loss of 164.36 kW and squared voltage deviations of 0.0235 p.u. Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and squared voltage deviations of 0.0182 p.u. in the system. Significant cost benefits for the EVCIs are also achieved, and an R-squared value of dynamic price predictions of 0.9999 is obtained. This would allow the charging station operator to make promotional offers for maximizing utilization and increasing profits.