Charging Facilities Research Articles

Economic analysis of parking, vehicle charging and vehicle-to-grid services in the era of electric vehicles

With the advances in electrical technologies (especially the vehicle-to-grid or V2G technologies), electric vehicles (EVs) now can be used as power storage. The latent power storage capacity in EVs can provide additional flexibility to the power system, and thus helps enhance the overall efficiency, stability and reliability of the power grid. With the V2G facility in place, EV users can choose to share their vehicles to the power grid as temporary storage while the vehicle is being parked or charged (termed as ‘V2G parking or charging service’). This study investigates the pricing and capacity decisions of parking, charging and V2G operators, subject to the EV users’ choice equilibrium. An EV user who demands parking or charging can choose the (conventional) dedicated parking or charging slot (managed by the parking or charging operator) or the slot of V2G facility such that his/her vehicle can be used by the power grid as temporary storage while being parked or charged (managed by the V2G operator). We formulate and analyze the EV user choice equilibrium subject to parking, charging and V2G service provision, and then investigate parking, charging and V2G operators’ optimal service fare and capacity decisions in different market regimes, where the operators may compete or cooperate with each other (e.g., charging and V2G facilities might be operated jointly). The main findings are as follows. (i) Introducing the V2G-based parking/charging service might earn a positive profit for the V2G operator and also benefit customers who request for parking or charging, but the parking and charging operators will suffer a loss. (ii) The competition between operators tends to reduce the service fares, while cooperation tends to increase the fares and yield more profits for the operators. (iii) The optimal capacity of parking, charging, or V2G facilities should be set to balance the marginal capacity acquisition cost and the marginal facility searching time cost. (iv) When V2G operator cooperates with parking/charging operator, if the additional gains of parking/charging operator through cooperation are smaller than that of V2G operator, the optimal service fare of parking/charging should be smaller, and thus will benefit the parkers/chargers (after V2G service is introduced). (v) The collaboration between parking (or charging) and V2G operators might also benefit the charging (or parking) operator. Overall, this study enhances the understanding in relation to parking and charging operators’ reactions to emerging V2G-based parking and charging services, and provides insights regarding how the V2G service should be planned and optimized.

An Activity Network Design and Charging Facility Planning Model Considering the Influence of Uncertain Activities in a Game Framework

In the planning of public charging facilities and the charging activity network of users, there is a decision-making conflict among three stakeholders: the government, charging station enterprises, and electric vehicle users. Previous studies have described the tripartite game relationship in a relatively simplistic manner, and when designing charging facility planning schemes, they did not consider scenarios where users’ choice preferences undergo continuous random changes. In order to reduce the impacts of queuing phenomenon and resource idleness on the three participants, we introduce a bilateral matching algorithm combined with the dynamic Huff model as a strategy for EV charging selection in the passenger flow problem based on the three-dimensional activity network of time–space–energy of users. Meanwhile, the Dirichlet distribution is utilized to control the selection preferences on the user side, constructing uncertain scenarios for the choice of user charging activities. In this study, we establish a bilevel programming model that takes into account the uncertainty in social responsibility and user charging selection behavior. Solutions for the activity network and facility planning schemes can be derived based on the collaborative relationships among the three parties. The model employs a robust optimization method to collaboratively design the charging activity network and facility planning scheme. For this mixed-integer nonlinear multi-objective multi-constraint optimization problem, the model is solved by the NSGA-II algorithm, and the optimal compromise scheme is determined by using the EWM-TOPSIS comprehensive evaluation method for the Pareto solution set. Finally, the efficacy of the model and the solution algorithm is illustrated by a simulation example in a real urban space.

Optimizing shared charging services at sustainable bus charging hubs: A queue theory integration approach

Integrating solar photovoltaic (PV) systems into bus charging infrastructure offers a promising solution to mitigate carbon emissions and reduce grid loads. However, a mismatch between the output of solar PV energy and the electric bus (EB) charging demand can arise due to the instability and intermittency of solar PV energy. To address this issue, this study introduces a novel shared charging business mode that allocates charging facilities to private electric vehicles (PEVs), leveraging idle infrastructure to maximize solar PV utilization. We develop an optimization framework to jointly determine the EB charging schedules and the allocation of charging facilities between EBs and PEVs within an EB network integrated with solar PV systems. Additionally, a loss queue model with time-dependent arrival rates is proposed to model the stochastic nature of PEV charging demand. A case study is conducted on a bus network in Beijing to validate our proposed operational strategy. The findings demonstrate that integrating shared charging in bus depots equipped with solar PV systems improves solar PV utilization from 79.30 % to 92.91 %. This integration reduces CO2 emissions and overall daily system costs by 7.93 % and 14.82 %, respectively, without negatively impacting the grid load during peak periods.

Evolutionary Game Analysis of Electric Vehicle Distribution Entities with Shared Charging Facilities

This study investigates the evolutionary game dynamics among electric vehicle distribution entities in the context of shared charging facilities, addressing the critical issue of inadequate charging resources. To understand the behavior of different stakeholders under government incentive policies, we develop an evolutionary game model involving a government department and two logistics enterprises (A and B). Through stability analysis, we explore equilibrium conditions of evolutionarily stable strategies (ESSs) for the tripartite evolutionary game. To ensure the robustness of our findings, we conduct a MATLAB simulation analysis to validate the analytical results. Our findings highlight that government subsidies, the costs incurred by logistics enterprises to share charging facilities, and the additional distribution income derived from this sharing are critical in determining whether the evolutionary game can achieve a stable equilibrium state. This research enables logistics companies to optimize the use of charging resources, lower operating costs, and enhance delivery efficiency. Additionally, government subsidy policies play a crucial role in encouraging logistics enterprises to engage in charging facility sharing, thereby fostering the sustainable development of the entire logistics industry. Based on these insights, the paper offers practical recommendations to further promote the sharing of charging facilities in electric vehicle distribution.

Application and Challenges of Battery Swapping Technology in the Development of New Energy Vehicle

Abstract: With the rapid growth of the new energy vehicle market, the construction of battery swapping stations has become an effective solution to the problem of insufficient charging facilities. Battery swapping technology can improve energy replenishment efficiency, alleviate pressure on the power grid, reduce charging costs through overnight charging, and increase battery utilization and profitability. However, battery swapping technology faces issues such as standardization, high costs, technical and safety challenges, and low market acceptance. It is suggested to promote standardization, government policy support, technological innovation and safety enhancement, the construction of a widespread battery swapping network, as well as innovation in business models and market promotion to promote the development of battery swapping technology.

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.

The potential promise and pitfalls of point-of-care viral load monitoring to expedite HIV treatment decision-making in rural Uganda: a qualitative study

BackgroundHIV treatment programs in Africa have implemented centralized testing for routine viral load monitoring (VLM), which may result in specimen processing delays inhibiting timely return of viral load results. Decentralized, point-of-care (PoC) VLM is a promising tool for expediting HIV clinical decision-making but remains unavailable in most African settings. We qualitatively explored the perceived feasibility and appropriateness of PoC VLM to address gaps along the viral load monitoring continuum in rural Uganda.MethodsBetween May and September 2022, we conducted 15 in-depth interviews with HIV clinicians (facility in-charges, clinical officers, nurses, counselors) and six focus group discussions with 47 peer health workers from three south-central Ugandan districts. Topics explored centralized VLM implementation and opportunities/challenges to optimizing routine VLM implementation with PoC testing platforms. We explored perspectives on PoC VLM suitability and feasibility using iterative thematic analysis. Applying the Framework Method, we then mapped salient constraints and enablers of PoC VLM to constructs from the Consolidated Framework for Implementation Research.ResultsClinicians and peers alike emphasized centralized viral load monitoring’s resource-intensiveness and susceptibility to procedural/infrastructural bottlenecks (e.g., supply stockouts, testing backlogs, community tracing of clients with delayed VLM results), inhibiting timely clinical decision-making. Participants reacted enthusiastically to the prospect of PoC VLM, anticipating accelerated turnarounds in specimen processing, shorter and/or fewer client encounters with treatment services, and streamlined efficiencies in HIV care provision (including expedited VLM-driven clinical decision-making). Anticipated constraints to PoC VLM implementation included human resource requirements for processing large quantities of specimens (especially when machinery require repair), procurement and maintenance costs, training needs in the existing health workforce for operating point-of-care technology, and insufficient space in lower-tier health facilities to accommodate installation of new laboratory equipment.ConclusionsAnticipated implementation challenges, primarily clustering around resource requirements, did not diminish enthusiasm for PoC VLM monitoring among rural Ugandan clinicians and peer health workers, who perceived PoC platforms as potential solutions to existing inefficiencies within the centralized VLM ecosystem. Prioritizing PoC VLM rollout in facilities with available resources for optimal implementation (e.g., adequate physical and fiscal infrastructure, capacity to manage high specimen volumes) could help overcome anticipated barriers to decentralizing viral load monitoring.

Variable-energy cocktail beam technology for investigating synergistic damage in nuclear materials on LEAF platform

This study addresses the critical issue of synergistic radiation damage in structural materials of fusion reactors, focusing on the interaction between the displacement defects and transmutation-produced hydrogen and helium. These effects are simulated and investigated by employing the advanced multi-beam ion implantation capabilities of the LEAF (Low Energy high intensity highly charged ion Accelerator Facility) platform. Firstly, high-intensity cocktail beams, such as “4He+ and 56Fe14+" and “4He+ and 58Ni15+", are generated and characterized successfully. Then, a complex radiation environment is mimicked within the fusion reactors by applying variable-energy irradiation. Secondly, similar penetration depths for different ions, which are crucial for studying synergistic effects, are obtained by precisely controlling the energy of the cocktail beams through the innovative energy modulation system of the LEAF platform. Finally, the post-irradiation analyses, performed by using the transmission electron microscopy (TEM) and nanoindentation, revealed distinct microstructural changes and alterations in material properties, providing insights into the degradation mechanisms under irradiation. This work not only generates diverse and high-intensity “cocktail” ion beams but also achieves rapid energy switching of the beams. Further, the work is expected to pave the way for the implementation of a novel multi-beam irradiation technique in advanced heavy-ion linear accelerators, and also to provide innovative experimental methods and technical support for studying the synergistic effects of nuclear materials.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

An improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

Considering Differentiated Pricing Mechanisms for Multiple Power Levels

With the development of supercharging technology, charging stations will face supercharging piles and fast-charging piles coexisting for a long time. The traditional unified service fee pricing model will face the problem of slow-charging vehicles occupying supercharging piles, and the advantages of supercharging facilities cannot be fully utilized. Considering factors such as station revenue, station utilization rate, and user willingness, this paper constructs a differentiated pricing model for multi-power-level charging facilities based on capacity and electricity service fees. It reveals the impact of this pricing model on the economy and user satisfaction of charging stations. In addition, a multi-objective optimization model is constructed and solved by a multi-objective particle swarm method to determine the pricing strategy of different power levels that adapt to the actual situation. The results show that the differentiated pricing mechanism can increase the station revenue by about 10.74% and the station stack power utilization rate by about 7.14%, and user satisfaction is higher, which is better than the conventional pricing mechanism. Therefore, the proposed charging pricing strategy can provide a decision-making reference for charging station operation and planning.

Sustainable development through the balancing of photovoltaic charging facilities and agriculture for energy harvesting

Photovoltaics (PV) and electric vehicles (EVs) provide viable alternatives for powering rural areas and promoting sustainable development. However, solar energy and agricultural land compete with each other, necessitating a balance between energy needs and land preservation. Despite the potential of agricultural PV charging stations, there is a lack of research on their operational models, policies, stakeholder interactions, and feasibility of development. This study combines renewable energy and charging infrastructure subsidy policies, utilizes a public-private partnership model, and employs evolutionary game theory to establish a theoretical framework exploring strategic interactions among energy operators, EV users, and farmers. The model considers the impacts of generation, charging, and agricultural elements, conducting techno-economic research. A case study conducted in Sioux Falls, South Dakota, USA, revealed subsequent findings: (1) The players' evolutionary stable approach ultimately converges to (1,1,1). When renewable energy subsidies are decreased by 50 %, the likelihood of active engagement for energy operators drops to 0, and for farmers, it falls to 0.8. (2) As the usage duration of charging facilities grows by 50 %, energy operators and EVUs make steady progress before reaching 0.2 cycles, but farmers encounter a delay after 0.4 cycles. (3) It is feasible to fulfil the yearly charging requirements for EVs in Sioux Falls and South Dakota using only 7.4 ha and less than 22 ha of agricultural land area, respectively. Investors in Sioux Falls have the potential to earn an annual income of $1.1085 million, generate 7906.07 MWh of electricity, and reduce GHG emissions by 6525.91 tons of CO2-equivalent. Although farmers generate higher annual revenue, it typically takes them over a decade to reach the point of breaking even, and their return on investment is comparatively lower than that of energy operators.

Optimal techno-economic assessment of isolated microgrid integrated with fast charging stations using radial basis deep learning

The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities. Consequently, and to complete the portfolio of the green environment, these fast-charging stations (FCSs) are designed using 100% of renewable energy sources (RESs). Thus, this paper proposes an optimization model for the techno-economic assessment of FCSs comprising photovoltaic and wind turbines with various energy storage devices (ESDs). In this regard, the FCS performance is evaluated using flywheels and super capacitors due to their high-power density and charging/discharging cycles and rates. Then, optimal sizing of these distributed generators is attained considering diverse technical and economical key performance indicators. Afterwards, the problem gets more sophisticated by investigating the effect of RES’s uncertainties on the selection criterion of the FCS’s components, design and capacity. Eventually, as an effort dedicated to an online energy management approach, a deep learning methodology based on radial basis network (RBN) is implemented, validated, and carried out. In stark contrast to conventional optimization approaches, RBN demonstrates its superiority by obtaining the optimum solutions in a relatively short amount of time.

Research on Sales of New Energy Vehicles Based on ARIMA Model

Firstly, this article identifies seven indicators based on electric vehicles: battery endurance, number of public charging facilities, government subsidies, gasoline prices, electricity prices, carbon emissions, and number of power battery companies. Spearman correlation analysis is conducted between indicator data and sales of new energy electric vehicles. The correlation coefficients of the first five indicators are all greater than 0.6, which is the main influencing factor. Then, the sales of new energy vehicles are predicted using a yearly forecasting method. Due to the close correlation between the sales of new energy vehicles and the five main indicators, the ARIMA time series model is first used to predict the annual quantity of the five indicators. Then, using these five indicators as inputs and sales as outputs, a BP neural network model is established to predict the annual sales of new energy vehicles in China to be 46051748 units in ten years. Finally, quantifying the score of ecological environment assessment using four evaluation indicators: sulfur dioxide, smoke emissions, ammonia nitrogen, and carbon dioxide. The entropy weight method was used to determine the weights of each indicator, and the results were 37.25%, 30.96%, 21.24%, and 10.55%, respectively. Then apply it to the TOPSIS model to obtain ecological environment scores for different years, and analyze the correlation between the degree of electrification and ecological environment scores. Finally, it can be concluded that the larger the market share of new energy vehicles, the better the ecological environment.

Development of Enhanced Computer Vision-based Monitoring Technology to Respond to Fire Accidents at Electric Vehicle Charging Stations

Owing to the recent popularity of electric vehicles (EVs), EV charging facilities have become mandatory, and charging stations that use electricity produced from renewable energy sources are being actively installed in buildings and apartments. As the number of these charging stations increases, the possibility of EV batteries overheating and causing a fire, which may spread and cause damage, also rises. However, current domestic and foreign studies do not suggest new and renewable energy supply or fire reduction measures for the supply of EVs. Therefore, to propose new-renewable energy EV charging stations for urban disaster prevention, in this study, we review EV charger installation cases, eco-friendly new renewable energy-based charging stations, and AI-based fire monitoring systems and analyze their characteristics through optimal operation. Through the fire prevention system developed in this study, we anticipate a novel approach for building a safer and more sustainable green energy supply system.

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

Modeling User Intentions for Electric Vehicle Adoption in Thailand: Incorporating Multilayer Preference Heterogeneity

Background: The automotive industry is pivotal in advancing sustainability, with electric vehicles (EVs) essential for reducing emissions and promoting cleaner transport. This study examines the determinants of EV adoption intentions in Thailand, integrating demographic and psychographic factors from Environmental psychology and innovation diffusion theory; Methods: Data from a structured questionnaire, administered to 4003 respondents at gas stations with EV charging facilities across Thailand, were analyzed using a Correlated Mixed-Ordered Probit Model with Heterogeneity in Means (CMOPMHM); Results: Findings indicate that younger adults, particularly those aged 25–34 years old and 45–54 years old, are more likely to adopt EVs, whereas conventional or hybrid vehicle owners are less inclined. Rural residency or travel also hinders adoption. Individuals with strong environmental values and openness to new technologies are more likely to adopt EVs; Conclusions: The proposed model quantified the relative importance of these factors and uncovered heterogeneity in user preferences, offering reliable and valuable insights for policymakers, EV manufacturers, and researchers. The study suggests targeted policies and enhanced charging infrastructure, especially in rural areas, and recommends leveraging environmental values and trialability through communication campaigns and test drive events. These insights can guide the development of targeted incentives, infrastructure expansion, communication strategies, and trialability programs to effectively promote wider EV adoption in Thailand and similar markets.

Tripartite evolutionary game and simulation analysis of electric bus charging facility sharing under the governmental reward and punishment mechanism

Electric vehicles (EVs) are environmentally friendly with the features of low noise levels and zero tailpipe emissions. However, limited charging facilities have restricted the large-scale development of EVs, and electric bus charging facility sharing is a promising solution to this issue. While reducing charging time and improving facility utilization, this measure involves the interests of the government, bus operators, and electric car owners, requiring a systemic analysis of their interactions. Therefore, this study constructs a tripartite evolutionary game model under the governmental reward and punishment mechanism. The model examines the strategic decision-making process, influential factors, and evolutionary stability of three players, followed by simulation analysis. The results show that: (1) The evolutionary system may exhibit three stable states: (0,0,0), (1,0,0) and (1,1,1), and enhance the initial ratio of the three stakeholders will help evolve towards the ideal state. (2) Increasing rewards and penalties can both be meaningful, when penalties exceed rewards, the measures can be more effective. (3) Bus operators are more sensitive to incentive and penalty measures than electric car owners, and governments should pay more attention to them in the implementation process. The findings can provide references for the policy formulation on electric bus charging facility sharing.

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.

Joint Optimal Design of Electric Bus Service and Charging Facilities

Joint Optimal Design of Electric Bus Service and Charging Facilities