Public Electric Vehicle Charging Research Articles

Policy interventions and urban characteristics in modeling electric vehicle charging infrastructure utilization

The surge in electric vehicles adoption necessitates understanding the impact of policy interventions on public electric vehicle charging infrastructure in urban areas. This research investigates the influence of pricing frameworks on the usage of public charging facilities by analyzing both behavioral and spatial attributes of these infrastructures. Utilizing open data from Palo Alto, United States, this study employs descriptive statistical methods and interpretable machine learning approaches to scrutinize the relationship between policy initiatives and charging behaviors. The analysis underscores the significance of spatial attributes on charging behaviors. Policy interventions yield noticeable alterations in charging metrics, with locations near commercial hubs showing higher utilization, while local and frequent users resist fee adjustments. The research emphasizes the necessity for customized strategies to optimize infrastructure development and management, offering a framework for policymakers and stakeholders in sustainable urban transportation. Future research should explore similar interventions in diverse urban settings using real-time data and advanced optimization techniques to better tailor policies to the unique characteristics of specific facilities.

Modeling Future Solar and Wind Energy Source Applications for Power Generation at Public Electric Vehicle Charging Stations in Airport Parking Areas Using HOMER-Grid

Modeling Future Solar and Wind Energy Source Applications for Power Generation at Public Electric Vehicle Charging Stations in Airport Parking Areas Using HOMER-Grid

The accessibility of public electric vehicle (EV) charging infrastructure: Evidence from the cities of Nottingham and Frankfurt

AbstractThe distribution of public electric vehicle (EV) charging infrastructure is a widespread approach for promoting EV adoption and decarbonising transportation. A significant amount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale. This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingham and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Nottingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode district (ZIP code) are examined: the percentage of apartment buildings/floor area ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in districts located near urban centres or those with high availability of amenities but also ensuring that users without home charging options are not left behind.

A two-layer planning method for location and capacity determination of public electric vehicle charging stations

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Modelling of A Hybrid Photovoltaic-Grid Source on Public Electric Vehicle Charging Station (PEVCS)

Modelling of A Hybrid Photovoltaic-Grid Source on Public Electric Vehicle Charging Station (PEVCS)

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

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.

The Design Concept of Integrated Pedestrian Sidewalk with Solar Plants as a Form of Green Campus Development at the Bali Land Transportation Polytechnic

Abstract An important aspect of creating a viable campus environment is providing safe and comfortable walking paths. Proper walking paths give some advantages for students, including improved physical and mental health, transportation efficiency, and increased Student satisfaction and happiness. The pedestrian sidewalk area at Bali Land Transportation Polytechnic (Poltrada Bali) covers 3800 m2, experiencing high pedestrian activity but lacking protective roofing to enhance pedestrian comfort. Based on the pedestrian sidewalk area, a solar power plant as support for the green canopy construction combined with shading vines is possible. The generated electrical energy serves as the power source for the Public Electric Vehicle Charging Stations (PEVCS) in the campus parking lot as a support for the electric vehicle acceleration program in Bali province. In addition to PEVCS, energy extraction surplus is used as alternative energy for street lighting facilities in the campus area. In this study, the design concept of a shaded pedestrian sidewalk has been carried out, with multiple benefits as solar plants using Delphi as a variable identification and validation tool, and SketchUp software to visualize design shapes aesthetically. The results show that a pedestrian comfort level of 94% was obtained and the maximum power generation of 934,800 Wp, thus it was able to meet the 887,832 W campus operational power reserve needs, street and garden lights of 9,728 W, and the power requirements of two PEVCS units of 22,000 W with a surplus of 15,240 W electric power.

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.

Electric vehicle public charging choices: a qualitative investigation

ABSTRACT Understanding how potential and existing electric vehicle users choose public charging is critical for the development of public-charging infrastructure. This study employs a qualitative approach guided by stated choice tasks to examine the decision-making processes regarding public charging. Through 20 interviews of existing and potential EV users in the UK, thematic analysis of responses pointed towards three areas: the choice context, attribute non-attendance and payment method. Participants sought detailed information about the circumstances under which they had to charge their vehicles. Attribute non-attendance meant that some participants paid more attention to specific attributes than others (e.g. location, price). Responses around the payment method pointed towards differences in relation to personal attitudes. This evidence suggests that charging choices are context-dependent, choices may be guided by specific attributes and individual attitudes play a role in these choices. These are also important considerations when designing a large-scale survey involving choice experiments and public charging.

Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method

The Indonesia government is actively promoting the adoption of electric vehicles, as detailed in the 2021-2030 Electricity Supply Business Plan. The state-owned electricity provider, PLN, is responsible for establishing Public Electric Vehicle Charging Stations (PEVCS). However, several of these stations have encountered malfunctions; notably, 82 of the 567 stations are classified as Unavailable, indicating they are non-functional. Research literature points to a financial loss of $34,000 from operational issues at PEVCSs. This research aims to helps management understand and prioritize disruption that leads to failures or damages at these stations. Method used is the Failure Mode and Effects Analysis (FMEA) method along with logistic regression to examine the disruptions at PEVCSs labeled as Unavailable. The data for this research comes from a six-month historical record of PEVCS disruptions. The variables utilized for logistic regression analysis include foundational variables from the FMEA methodology—Severity, Occurrence, and Disturbance—complemented by two supplementary variables: the speed and age of the PEVCS. Result was found that three out of twelve types of disruptions have a high likelihood of failure, specifically issues with Device Communication, Connectivity, and Emergency Stop functions. A disruption is deemed likely to cause failure if its probability exceeds 50%.

Development of Public Electric Vehicle Charging Stations (SPKLU) in Makassar as an Electric Car Supporting Means

Development of Public Electric Vehicle Charging Stations (SPKLU) in Makassar as an Electric Car Supporting Means

Risk Analysis of Electricity Demand at Public Electric Vehicle Charging Stations (SPKLU): CVaR Model Approach

The electricity demand at Public Electric Vehicle Charging Stations (SPKLUs) exhibits significant volatility, which is driven by several aspects including electricity demand patterns at specific time intervals, load variability, SPKLU capacity, and other related factors. The variability of these swings can present hazards for SPKLU operators in relation to energy administration as well as operational and financial hazards. The objective of this study is to assess the risk associated with energy demand fluctuation at SPKLUs by employing the Conditional Value-at-Risk (CVaR) model technique. CVaR, or Conditional Value at Risk, is a quantitative measure of risk that calculates the predicted loss value in the most unfavorable situation. It is commonly employed to enhance the risk management approach of SPKLUs. The electricity demand at SPKLU exhibits significant volatility, with an average fluctuation of 10.15% and a standard deviation of 49.67%. The CVAR, calculated at -121.19% for a confidence interval of 1%, represents the maximum potential loss that could be experienced during worst-case electrical demand conditions, highlighting the substantial fluctuations in demand. The study initially implemented the CVaR model to analyze power demand at SPKLU, providing novel perspectives on risk reduction for critical infrastructure and proposing unique strategies for managing demand fluctuations in a reliable and efficient manner. The results also offer comprehensive insights into risk exposure and facilitate the formulation of well-informed and strategic risk management plans.

A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Assessing the spatial distributions of public electric vehicle charging stations with emphasis on equity considerations in King County, Washington

This study assessed the availability and accessibility of public electric vehicle (EV) charging stations at the census tract level in King County, Washington, USA. A gravity model was used to measure the accessibility of stations in each tract while considering the competition among tracts for charging infrastructure. Unlike prior studies, we evaluate current distribution from various equity viewpoints, clearly defining these perspectives and investigating their implications for charging distribution. The findings revealed significantly higher accessibility of public stations in areas with a lower share of single-family homes, a higher share of commercial areas, and proximity to major roadways, irrespective of population and EV adoption rates. Moreover, we found that the current distribution is consistent with utilitarian and capabilities-based theories of equity. Spatial tools such as Moran's I and spatial regression were used to analyze the spatial disparity of the proposed accessibility measure, showing the existence of a highly clustered spatial distribution. This study offers valuable insights into integrating equity considerations in assessing accessibility to transportation facilities, especially EV charging stations. Such an approach can facilitate the promotion of EV adoption across diverse groups, a critical factor in achieving sustainable city development goals by mitigating carbon emissions and enhancing mobility.

Coin-Operated Public Electric Vehicle Charging Station with Solar Panels as the Energy Source

Electric transport is becoming increasingly popular as another option to use environmentally friendly energy in the form of transport. To support the development of electric transport, efficient and accessible charging infrastructure is required. Public Electric Vehicle Charging Stations (SPKLU) have become a major part of supporting long-distance travel and reducing the impact of greenhouse gas emissions on the environment. To increase the availability of SPKLUs and expand accessibility for electric vehicle users, this research aims to design an innovative coin-based SPKLU system that also utilizes solar energy through solar panels. The station can have a smaller environmental effect and lower operating costs by employing solar energy as its primary source. The examination included a review of the economy as well. As a result, those in charge, business owners, and communities who want to promote the usage of electric vehicles find it to be a desirable alternative. As a result of this research, it was found that a full charge of a 20Ah battery only requires a coin of Rp 7000

Analysis of PT PLN's Innovative Strategy in Supporting Electric Vehicles in Indonesia

Global transformation towards sustainable mobility, the electric vehicle (EV) industry plays an important role in creating a cleaner and environmentally friendly future with the journey towards sustainable mobility, PT PLN (Persero) plays an important role in supporting the electric vehicle ecosystem in Indonesia. With more than a thousand Public Electric Vehicle Charging Stations (SPKLU) operating by the end of 2023, PT PLN confirms its commitment to accelerate the transition to clean mobility. However, technical challenges, change management, and organizational culture are obstacles that must be overcome. This research method uses a qualitative approach to understand PT PLN's strategy in supporting the electric vehicle ecosystem in Indonesia. PT PLN's strategy in supporting clean mobility includes infrastructure development, strategic partnerships, home charging installation initiatives, and the application of digital technology. Through holistic analysis, it highlights the importance of effective change management and an inclusive organizational culture. The results of this research provide insight into best practices in supporting the electric vehicle ecosystem and provide input for designing more effective policies. By continuing to strengthen infrastructure, develop partnerships, empower communities, technological innovation and good change management, PT PLN can strengthen its role as a leader in clean mobility and contribute to global sustainable development goals.

Spatial heterogeneity analysis on distribution of intra-city public electric vehicle charging points based on multi-scale geographically weighted regression

The rapid rollout of electric vehicle (EV) charging infrastructure is critical for enhancing EV penetration and building an efficient e-mobility system. However, research concerning the impact of the built environment on the deployment of public EV charging points (EVCPs) and its spatial variations remains insufficient. To address this gap, an innovative perspective to assess the performance of public EVCP spatial distribution is firstly developed considering service accessibility and capacity. Using multi-source data, including the POI data, second-hand house data and EVCP static data by the end of 2021, multiscale geographically weighted regression (MGWR), GWR and OLS are conducted in the Beijing case study to assess the influence of the built environment and socio-economic characteristics on the distribution of intra-city public EVCPs. Two sets of comparison are conducted, one for EVCPs with different charging powers (AC and DC) and another for those with varying charging capacities, categorized as distributed and centralized EVCPs. MGWR performs better than OLS and GWR statistically with the largest adjusted R2 and lowest AICc and RSS. The results demonstrate an imbalanced and spatially diverse deployment of EVCPs, with DC and distributed EVCPs displaying a clear concentration trend within the densely populated core area. Besides, 9 variables have emerged as statistically significant factors which vary across analysis groups in significance, coefficients, and bandwidths, showing complex interaction mechanism with EVCPs of various locations and attributes. The conclusions provide insights for policymaking aimed at planning and deploying public EVCPs in megacities.

Spatial Accessibility of Public Electric Vehicle Charging Services in China

Decarbonizing the transport sector using electric vehicles (EVs) is a vital pathway for China to achieve the carbon peak and carbon neutrality goals. Despite the unprecedented growth of EV diffusion in China, little information is available for the spatial accessibility of public electric vehicle charging services (EVCSs). This study developed an applicable accessibility measurement framework to examine the city-level accessibility of EVCSs in China using the Gaussian two-step floating catchment area (G2SFCA) method. G2SFCA takes the EV charging stations with charging piles as supply and the EV ownership data as demand. The results indicate that (1) the eastern region of China has the highest density of EV charging stations (69.1%), followed by the central region, while the western region has the lowest density; (2) the spatial accessibility of EVCSs has a different pattern, where the central region has the highest accessibility, followed by the eastern and western regions; (3) the spatial mismatch between EVCSs and EV diffusion in the eastern region is larger than that of the other two regions, which may be attributed to the suboptimal layout of EV charging stations and the inconsistent pace between EV penetration and EV charging station construction; and (4) there is a significant spatial inequity in the accessibility of EVCSs across both all three regions and the entirety of China, with the western region exhibiting the highest inequity, followed by the central and eastern regions. Based on these findings, policy implications are drawn for different regions in China, which may aid policymakers in crafting strategic policies and subsidy programs to foster the advancement of EVCSs.

A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

This paper investigates the electric vehicle (EV) charging scheduling problem for public EV charging stations (EVCSs) that can accommodate heterogeneous charging demands, aiming to flatten the aggregate load on the power grid and reduce the peak demand. In contrast to existing works that mainly focus on a single scheduling strategy, a two-level hierarchical charging scheduling method is proposed, which includes an online booking system (OBS) and a pricing-based charging control system (PCCS). Specifically, the implementation of OBS can reduce the service capacity of bookable chargers, whereas PCCS can facilitate the redistribution of charging demand from peak to off-peak time periods. Moreover, the nonlinear charging profile of the battery is incorporated into the model to better reflect the real charging process. A deep reinforcement learning (DRL)-based approach with a discrete Markov decision process (MDP) formulation is designed to find the optimal scheduling solution, where deep Q-network (DQN) and deep deterministic policy gradient (DDPG) are combined to handle the hybrid action space with both discrete and continuous actions. A comprehensive set of experiments is carried out to examine the effectiveness of the developed two-level scheduling scheme. The results indicate that the proposed method improves the scheduling effectiveness compared to single-strategy methods by 6.7% to 49.44%.