Utilization Of Charging Stations Research Articles

Recent Advances in Fast-Charging Sodium-Ion Batteries.

Sodium-ion batteries (SIB), stemming from the abundance of sodium resources and their cost-effectiveness, have positioning them favorably a potential candidate for stationary energy storage and public electric vehicles. As an intermediary of the grid system and output terminals of the charging station, fast-charging performance has actually become a crucial metric, which greatly relates to the charging station utilization and cost- and time-efficient. Besides, the fast-charging capacity is also relevant to the long-term stable operation public transportation. Given the remarkable advancements in fast-charging SIBs reported recently, a review about this topic and scope is timely and important at present. In this study, the bottlenecks of fast-charging SIBs are first assessed, after that, a comprehensive overview of the employed strategies for improving the fast-charging capacities of SIBs from three aspects: structures design, reaction mechanism regulation, and optimization of solvation structure and interfacial property is given. Finally, challenges and prospects for further research toward fast-charging SIBs are proposed. The authors hope this review will provide a deep understanding of the design principles of fast-charging SIBs and inspire more endeavors to conquer the practicability issue of SIBs in energy storage fields.

Priority-Based Pre-Booking System for Efficient Electric Vehicle Charging Station Utilization

Priority-Based Pre-Booking System for Efficient Electric Vehicle Charging Station Utilization

Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.

EVCSeer: An Exploratory Study on Electric Vehicle Charging Stations Utilization via Visual Analytics.

Promoting the development of electric vehicles requires the widespread deployment of charging infrastructure, which poses numerous technical and financial constraints. Despite extensive research focusing on optimizing charging station locations, few studies have accounted for charging station utilization and the factors that influence it. This study aims to evaluate charging station operations and explore charging station utilization to inform planning and facilitate better utilization of funds for expanding charging infrastructure. We present EVCSeer, a visual analytics system that utilizes representative predictive models and well-designed visualizations to analyze factors affecting charging station utilization, compare deployment strategies, and optimize utilization. The system also enables "what-if" analysis of charging station deployments. Two case studies, expert interviews, and a qualitative user study support the validity and usefulness of EVCSeer.

Enhancing the Efficiency of Electric Vehicles Charging Stations Based on Novel Fuzzy Integer Linear Programming

The electric vehicles (EVs) charging stations (CSs) at public premises have higher installation and power consumption costs. The potential benefits of public CSs rely on their efficient utilization. However, the conventional charging methods obligate a long waiting time and thereby deteriorate their efficiency with low utilization. This paper suggests a novel fuzzy integer linear programming and a heuristic fuzzy inference approach (FIA) for CSs utilization. The model introduces the underlying fuzzy inference system and a detailed formulation for obtaining the optimal solution. The developed fuzzy inference incorporates the uncertain and independent available power, required state-of-charge, and dwell time from the power grid and EVs domains and correlates them into weighted control variables. The FIA automates the service provision for the EVs with the most urgent requirements by resolving the objective function utilizing the weighted control variables, thereby optimizing the waiting time and the CSs utilization. To evaluate the effectiveness of the proposed FIA, several case studies were conducted, corresponding to different parking capacities and installations of CSs. Moreover, the simulations were conducted on EVs with varying battery capacities, and their performance was evaluated based on several metrics, including average waiting time, utilization of CSs, fairness, and execution time. The simulation results have confirmed that the effectiveness of the proposed FIA scheduling method is considerably higher than that of the other methods discussed.

A large-scale empirical study on impacting factors of taxi charging station utilization

A large-scale empirical study on impacting factors of taxi charging station utilization

Electric Vehicle User Behavior: An Analysis of Charging Station Utilization in Canada

For a user-centered deployment of electric vehicle supply equipment (EVSE) infrastructure, it is vital to understand electric vehicle user charging behavior. This study identifies user behavioral patterns by analyzing data from more than 7000 charging stations in Canada, comparing residential vs. public Level 2, and public direct current fast (DCFC) vs. public Level 2 charging. A novel algorithm, CHAODA, was applied to identify differences between DCFC and other Level 2 charging options. Through a multivariate and holistic methodology, various patterns emerge, identifying differences in the utilization and seasonality of different EVSE types. The study provides evidence of an “EV Duck Curve” that amplifies the baseline of the power production “Duck Curve,” confirming future challenges for grid stability. Implementations of this study can support future EVSE infrastructure planning efforts and help improve the overall service of electric vehicle supply equipment and grid stability.

Evaluation of Energy Supply Technologies for Autonomous Cargo Bike-Sharing System

Due to technological advancements and the ever-increasing demand for sustainable modes of transport, research on autonomous driving, shared mobility, and micro-mobility has gained interest, particularly in urban mobility. To facilitate such transport modes, systems like bike-sharing, car-sharing, and shared autonomous cars are implemented. However, the systems mentioned above have traffic congestion, a lack of parking space availability, and on-demand service concerns. To address the aforementioned challenges, we intend to integrate an on-demand shared-use, self-driving cargo-bikes service (OSABS) as a new mode of transportation into the urban mobility ecosystem. The selection of energy supply technology for self-driving cargo bikes is essential for making the OSABS system more environment-friendly and sustainable. Therefore, this paper aims to identify a suitable energy supply technology for our self-driving cargo bikes. Following that, we compare each technology using key performance indicators (KPIs), such as service level, implementation cost, energy consumption cost, charging station utilization, and profit analysis. Furthermore, we use the agent-based simulation model to experiment with the identified energy supply technologies, such as battery-powered and hydrogen fuel cell-powered autonomous cargo bikes.

Public electric vehicle charging station utilization in the United States

Public electric vehicle charging station utilization in the United States

Comparing the levelized cost of electric vehicle charging options in Europe

With rapidly decreasing purchase prices of electric vehicles, charging costs are becoming ever more important for the diffusion of electric vehicles as required to decarbonize transport. However, the costs of charging electric vehicles in Europe are largely unknown. Here we develop a systematic classification of charging options, gather extensive market data on equipment cost, and employ a levelized cost approach to model charging costs in 30 European countries (European Union 27, Great Britain, Norway, Switzerland) and for 13 different charging options for private passenger transport. The findings demonstrate a large variance of charging costs across countries and charging options, suggesting different policy options to reduce charging costs. A specific analysis on the impacts and relevance of publicly accessible charging station utilization is performed. The results reveal charging costs at these stations to be competitive with fuel costs at typical utilization rates exhibited already today.

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

Abstract The expansion of the public charging infrastructure for electric vehicles is seen as central to the development of electric mobility in many countries. Although national studies of charging infrastructure utilization based on real-world data would be a sound basis for demand planning, such studies are scarce. Using Switzerland as an example, this study examines the spatial and temporal patterns of charging infrastructure utilization. To this end, detailed, nationwide, real-time utilization data from 3086 electric vehicle supply equipment units (EVSEs) at electric vehicle charging stations were collected over a period of several months and analyzed exploratively and statistically. The maximum average utilization rate of the EVSEs surveyed during the study period is between 14% and 16%, depending on the day of the week and time of day. Most charging occurs Monday through Friday during peak working hours and on Saturday during the day. The median utilization time is higher in the largest cities than the statewide average. Charging stations along major transit routes do not have higher utilization rates than in other locations. The results suggest that public charging infrastructure is used primarily in cities and agglomeration during work hours. The findings from this study may help plan and make better use of funding to expand charging infrastructure.

Electric Vehicle Public Charging Infrastructure Planning Using Real-World Charging Data

The current increase of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage. To make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. An essential factor for the degree of utilization is the placement of a charging station. Therefore, the initial site selection plays a critical role in the planning process. This paper proposes a charging station placement procedure based on real-world data on charging station utilization and places of common interest. In the first step, we correlate utilization rates of existing charging infrastructure with places of common interest such as restaurants, shops, bars and sports facilities. This allows us to estimate the untapped potential of unexploited areas across Germany in a second step. In the last step, we employ the resulting geographical extrapolation to derive two optimized expansion strategies based on the attractiveness of locations for electric vehicle charging.

Public Electric Vehicle Charging Station Utilization in the United States

Public Electric Vehicle Charging Station Utilization in the United States

Queue Based Dynamic Charging Resource Allocation and Coordination for Heterogeneous Traffic in an Electrical Vehicle Charging Station

ABSTRACT Electric vehicle (EV) fast-charging stations (CSs) with innovative operation management strategies can help to meet the growing EV charging needs. Despite the short charging time associated with the fast charging of EVs, massive deployment of plugged-in EVs for fast charging triggers network surges in the distributed network. Nevertheless, these challenges can be effectively addressed with appropriate EV admission control, charging resource allocation, and coordinated charging strategies. This work proposes an innovative EV-CS operation mechanism that maximizes the CS profit by allowing fast charging EVs to access the CS opportunistically without stressing the distributed network. Furthermore, a queue is employed in the dynamic resource allocation process to favor more fast charging requests through buffering non-critical slow charging EVs, as fast charging users would otherwise be blocked or forcibly terminated. Performance of the proposed priority-based charging coordination is analyzed in terms of the optimum utilization of demand limit, charging completion rate, and charging station utilization. Therefore, it keeps the average charging station utilization above 90% at higher arrival rates of EVs while the distribution transformer is being loaded only up to 50% of its rated capacity. Moreover, the blocking and forced termination probabilities of fast charging users are used to evaluate the service quality of EV charging. The presented dynamic resource allocation and charging coordination strategies for heterogeneous EV traffic maximize the CS profit while assuring quality service to EV users without stressing the distributed network.

Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

Solving Overstay and Stochasticity in PEV Charging Station Planning With Real Data

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the “overstay” problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this “overstay.” This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service-an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking strategic interchange into account. To our best understanding, this has not been studied in the literature. With interchange, the objective is to enhance the charger utilization rate and, thus, reduce the number of chargers. This potentially reduces the capital investment and operational cost. A novel power/energy aggregation model is proposed, and a chance-constrained stochastic programming planning model with interchange is developed for a public charging station to incorporate customer demand uncertainties. Numerical experiments are conducted to illustrate the performance of the proposed method. Simulation results show that incorporating strategic interchange operation can significantly decrease the number of chargers, enhance utilization and economic efficiency.

A data-driven statistical approach for extending electric vehicle charging infrastructure

Current trends suggest that there is a substantial increase in the overall usage of electric vehicles (EVs). This, in turn, is causing drastic changes in the transportation industry and, more broadly, in business, policy making, and society. One concrete challenge brought by the increase in the number of EVs is a higher demand for charging stations. This paper presents a methodology to address the challenge of EV charging station deployment. The proposed methodology combines multiple sources of heterogeneous real-world data for the sake of deriving insights that can be of a great value to decision makers in the field, such as EV charging infrastructure providers and/or local governments. Our starting point is the business data, ie, data describing charging infrastructure, historical data about charging transactions, and information about competitors in the market. Another type of data used are geographical data, such as places of interest located around chargers (eg, hospitals, restaurants, and shops) and driving distances between available chargers. The merged data from different sources are used to predict charging station utilization when EV charging infrastructure and/or contextual data change, eg, when another charging station or a place of interest is created. On the basis of such predictions, we suggest where to deploy new charging stations. We foresee that the proposed methodology can be used by EV charging infrastructure providers and/or local governments as a decision support tool that prescribes an optimal area to place a new charging station while keeping a desired level of utilization of the charging stations. We showcase the proposed methodology with an illustrative example involving the Dutch EV charging infrastructure through the period from 2013 to 2016. Specifically, we prescribe the optimal location for new ELaadNL charging stations based on different objectives such as maximizing the overall charging network utilization and/or increasing the number of chargers in scarcely populated areas.

Multi-Objective Distribution Network Expansion Incorporating Electric Vehicle Charging Stations

The paper develops a multi-objective planning framework for distribution network expansion with electric vehicle charging stations. Charging loads are modeled in the first place, and then integrated into the optimal distribution network expansion planning. The formulation is extended from the single objective of the economic cost minimization into three objectives with the additional maximization of the charging station utilization, and maximization of the reliability level. Compared with the existing models, it captures the interactive impacts between charging infrastructures planning and distribution network planning from the aspects of economy, utilization, and reliability. A multi-stage search strategy is designed to solve the multi-objective problem. The models and the strategy are demonstrated by the test case. The results show that the proposed planning framework can make a trade-off among the three objectives, and offer a perspective to effectively integrate the network constraints from both the transportation network and distribution network.