Search

The increasing popularity of electric vehicles (EVs) due to their limited onboard battery power and eco-friendliness has created a pressing need for widespread availability of charging stations. High-speed charging stations, in particular, can strain the power grid by potentially overloading it during peak hours, causing sudden power shortages and voltage drops. This research paper examines the ANSYS twin-Builder modeling approach to create a comprehensive model of an integrated system consisting of photovoltaic (PV) power generation, battery energy storage (BES), and a multiport device-based charging station. The integration of these components enables advanced stabilization techniques such as power gap equalization, peak shaving, valley filling, and voltage sag correction. The primary objective is to reduce the burden on the power grid by meeting the daily charging demand using sufficient daylight PV output. The simulation results have been validated to showcase the benefits of employing the PV-BES configuration in multiport EV charging circuits across various operational modes. To further improve performance, a transfer system is introduced, which incorporates gate turn-off thyristors and insulated-gate bipolar transistors into the EV charging station. The paper also analyzes power losses and efficiency for different modes and configurations, comparing them with simulations of conventional Si device-based charging networks. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive review of the impacts and imminent design challenges concerning such EV charging stations that are based on solar photovoltaic infrastructures is presented, which is based on state-of-the-art frameworks for PV-powered charging stations and the latest case studies. The main factors that are targeted in this review are the management of an EV charging system that is a composite of PV and public grid, as well as a charging system business model that can affect consumer behavior by charging at different rates and the best design infrastructure for a PV electric vehicle charging services. The economic, environmental, and social impacts caused by the installation of PV-driven charging infrastructures are also compared. Moreover, the framework for recently emerging vehicle-to-grid and vehicle-to-home services, as well as the integration of these frameworks, is discussed. Finally, survey results for future EV demand and its public acceptance are presented. Appertaining to these issues, the case studies highlight that EV transportation between parking lots and charging stations is necessary to make the best use of the available charging stations. Statistically, in this study, it was inferred that there are no limitations on the amount of EV battery capacity that can be stored, and users of EVs can charge in both slow and fast modes. Furthermore, V2G systems are not suitable for widespread industrial use. Moreover, finding solutions and overcoming numerous obstacles remain unaddressed issues, and the size of the EV industry must reach a certain level to make profitable sales of V2B, V2H, V2G, and other V2X systems. The collected statistics indicate that although respondents generally have a favorable opinion of the mobility applications powered by photovoltaic cells, it seems unlikely that they will use them anytime soon. [ABSTRACT FROM AUTHOR]

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments. [ABSTRACT FROM AUTHOR]

As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user's present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles. [ABSTRACT FROM AUTHOR]

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs' charging behavior. However, this task presents several challenges due to the dynamic nature of EVs' usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs' plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework's effectiveness, and the results demonstrate its proficiency in predicting the EVs' plugged-in status, with XGBoost's classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs' power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape. [ABSTRACT FROM AUTHOR]

The electrification of transport is expected to progressively replace significant shares of light duty mobility, especially in large cities. The European Alternative Fuel Infrastructure Regulation (AFIR) aims to drive the adoption of electric mobility by establishing specific targets for charging point deployment. Innovative charging concepts may complement and accelerate the uptake of this fundamental part of the urban mobility transition. In this paper, one such innovative concept is described and its potential impact is assessed. The core idea involves integrating charging points into existing city tramway infrastructures. Turin's tramway network is taken as a representative case study. The proposed technical solution encompasses a charging hub powered by four isolated DC/DC converters of 50 kW, directly connected to the DC tramway distribution line. Three of these constitute the heart of a 150 kW charger, while the fourth acts as voltage regulator. This native DC installation greatly simplifies the architecture of the DC chargers. Using a conservative approach, it was estimated that a single recharging station could charge more than 60 vehicles daily. This highly scalable and replicable solution, with the potential for over 100 conversion substations across Italy, would enable the installation of numerous high-power chargers in urban settings. Furthermore, additional benefits could be realized through enhanced recovery of kinetic energy from trams, which is currently dissipated on-board. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) travel planning is a complex task that involves optimizing both the routes and the charging sessions for EVs. Existing algorithms rely on single-objective optimization, which limits their ability to consider EV users’ multiple, often conflicting objectives. In this paper, we introduce a new, genuinely multi-objective approach to EV travel planning, which can find Pareto sets containing multiple EV travel plans optimized simultaneously for multiple objectives. We focus on the bi-objective optimization for travel time and cost. To our knowledge, our algorithm is the first to perform such a genuine multi-objective optimization on realistically large country-scale problem instances involving 12,000 charging stations. We implemented our approach into a fully operational prototype application and extensively evaluated it on real-world data. Our results show that our approach can achieve practically usable planning times with only a minor loss of solution quality despite the very high computational complexity of the problem. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the optimal sizing of a proposed photovoltaic (PV)-battery grid-connected system for fast charging station of electric vehicles (FCSEVs) in Cairo, Egypt. The key objectives of the formulated optimization problem are to minimize the total system cost and to guarantee the validity of the energy balance principle within the considered system. The paper also develops a novel energy management strategy, which is designed based on two widely adopted pricing strategies, for optimally managing the power flow within the considered system with the purpose of reaching the optimum sizes of the considered system components. In addition, the paper suggests a modification to the Snake optimization (SO) algorithm, which is used to solve the formulated optimization problem using MATLAB software. Moreover, a comparison is conducted among the proposed modified Snake optimization (MSO) algorithm and four other common meta-heuristic optimization algorithms to validate the viability of the suggested MSO algorithm in achieving the desired eventual aims of the sizing process. Furthermore, a techno-economic study is carried out to assess the economic viability of the proposed system for each of the two adopted pricing strategies over the project lifetime. The yielded results indicated that the developed energy management strategy is capable of managing the energy flow within the considered system effectively. Also, they demonstrated that the suggested MSO algorithm can yield the best optimal solutions for the formulated optimization problem for each of the two adopted pricing strategies compared to the other considered optimization algorithms.

Reverse logistics plays a critical role in sustainable development and circular economy by transforming waste into value. However, transporting large volumes of waste and recyclables generates significant carbon emissions. This paper explores an industry-led strategy for charging infrastructure development, which integrates the reverse logistics network designs with a network of charging stations to promote electric trucks (E-trucks) adoption and reduce carbon footprints in transportation. Our model optimizes operational costs while ensuring accessibility to charging infrastructure. A case study in Norway demonstrates that, despite higher operating costs due to additional charging stations, the adoption of E-trucks can decrease total reverse logistics costs by 0.18%—2.37% and cut carbon emissions by 16.3%—17.6%. These findings support the economic viability and environmental benefits of this strategy for advancing sustainable practices in transportation, and it further highlights the interdependencies between logistics network design and charging infrastructure planning. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) provides possibilities from simple displays to tools improving service in the production process. It has found justification in the field of crisis/critical situation management and also in the commercial sphere. The technologies of the digital world together with AR are directed towards the user environment and the operation of technical devices does not deal with the essence of the problem. Electric vehicle (EV) charging stations (CHS) are considered to be sources of electromagnetic fields, as are also inverters in EVs. Until now, there are standards only for stable sources, which EV is not. There are several standards for CHS, from guidelines to regulations in a specific country. The magnetic field (MF), as a component of the electromagnetic field, carries information about the state of the CHS and the environment around the EV during its charging. Information provided to the operator about the state of the environment around him when charging EV is currently at a minimal level. There is no application yet that provides a display of MF information for the operator. AR in the form of the AR Infinity platform provides such an option. The article points out the way of creating information tools using simple devices in an AR environment with a mobile phone application. [ABSTRACT FROM AUTHOR]

With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the realtime charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region. [ABSTRACT FROM AUTHOR]

In response to escalating environmental concerns driven by greenhouse gas emissions, Pakistan, amid accelerated climate change and deteriorating air quality, struggles with power crisis. Our purpose of this research was to develop a pivotal strategy to address the power crisis and provide clean transportation facilities by involving power transitioning from fossil fuel vehicles to electric vehicles (EVs). We aimed to establish EV charging stations powered by renewable sources like solar and wind energy using grid to vehicle (V2G) mechanism. Utilizing MATLAB Simulink, an optimal electric vehicle charging system with a Level 2 fast charging mechanism was designed, aiming to significantly reduce greenhouse gas emissions from both the transportation and energy sectors. This framework aligned with global trends in climate change mitigation, providing developing countries like Pakistan with a practical solution. The results indicated a 10-kW, AC power output at 240 V coupled with an ideal 50 kWh EV battery rating, which was achieved for EV charging. The output parameters, including current voltage and power output of solar PV, micro wind, and battery levels, were used before and after the incorporation of a boost converter. Consequently, the application of a boost converter and proportional integral (PI) controller resulted in low overshoot and steady state output parameters of the proposed system. Also, the battery backup helped to optimize the power output for load driven EVs. [ABSTRACT FROM AUTHOR]

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work's objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach's competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive review of the impacts and imminent design challenges concerning such EV charging stations that are based on solar photovoltaic infrastructures is presented, which is based on state-of-the-art frameworks for PV-powered charging stations and the latest case studies. The main factors that are targeted in this review are the management of an EV charging system that is a composite of PV and public grid, as well as a charging system business model that can affect consumer behavior by charging at different rates and the best design infrastructure for a PV electric vehicle charging services. The economic, environmental, and social impacts caused by the installation of PV-driven charging infrastructures are also compared. Moreover, the framework for recently emerging vehicle-to-grid and vehicle-to-home services, as well as the integration of these frameworks, is discussed. Finally, survey results for future EV demand and its public acceptance are presented. Appertaining to these issues, the case studies highlight that EV transportation between parking lots and charging stations is necessary to make the best use of the available charging stations. Statistically, in this study, it was inferred that there are no limitations on the amount of EV battery capacity that can be stored, and users of EVs can charge in both slow and fast modes. Furthermore, V2G systems are not suitable for widespread industrial use. Moreover, finding solutions and overcoming numerous obstacles remain unaddressed issues, and the size of the EV industry must reach a certain level to make profitable sales of V2B, V2H, V2G, and other V2X systems. The collected statistics indicate that although respondents generally have a favorable opinion of the mobility applications powered by photovoltaic cells, it seems unlikely that they will use them anytime soon.

As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user’s present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.

The demand for electric vehicles is increasing due to the recent focus on reducing greenhouse gas emissions. Hence, the number of electric-vehicle charging stations must be increased to meet the growing demand for electric vehicles. To this end, supportive policies are being established to increase the deployment of electric vehicle charging stations worldwide. On the other hand, it is not possible to subsidize all facilities simultaneously. This study developed a model to prioritize the deployment of electric vehicle charging stations. The Republic of Korea was analyzed as a case study region to develop this model, and the correlations between the amount of charging energy of electric vehicle charging stations and socioeconomic factors (traffic volume, population, number of electric vehicles, and land value) were analyzed. The correlations were analyzed differently depending on the purpose (e.g., residential or commercial) of the facilities where electric vehicle charging stations were installed. Correlation analysis was conducted to determine the factors that affect the amount of charging energy, and a model was developed to prioritize the deployment of electric vehicle charging stations through a genetic algorithm. A model with a correlation of more than 0.2 was developed, except for residential facilities with slow chargers and public institutions with slow chargers. The results of this paper can help identify the key factors to be analyzed by facility use when installing electric vehicle charging stations and determine the priorities for subsidizing the deployment of electric vehicle charging stations.

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users' dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users' dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified. [ABSTRACT FROM AUTHOR]

In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model's applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model. [ABSTRACT FROM AUTHOR]

The large-scale adoption of electric vehicles will require a charging infrastructure that meets the new needs that will arise. Currently, the charging infrastructure for electric vehicles is still in the early stages of development, not least because of the low number of electric vehicles in use. However, there are still many questions to be answered when it comes to standardization in terms of connectors, DC or AC charging, and power, as well as both operational and economic issues. Although this topic has been the subject of numerous studies over the last ten years, there are still gaps to be filled, particularly with regard to the mix of different recharging strategies (normal, accelerated, fast, induction-track, etc.), as well as the economic and operational aspects. Moreover, the relationship between users and private cars is changing rapidly, and charging behaviors are not yet well established. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) have become a viable choice for cutting carbon emissions in the field of road transportation. EVs will be incorporated into the electrical grid at an increasing rate in the near future. EVs themselves are becoming immense power consuming load on the power grid system as a result of the growing number of EVs, mostly light-duty EVs. In addition to posing major technical risks such power quality issues, voltage swings, harmonic injection, battery deterioration, and grid instability, the unplanned installation and rapid adoption of charging stations might impede the smooth functioning of the power distribution system. Therefore, this paper analyzes and summarizes the potential issues and solutions for Fast Wireless Charging (FWC) that allows for the wireless transmission of very high-power levels (>20 kW) from the power grid to explore the impact of EV charging on the power system network. This need can have far-reaching effects on the load profile, operation, and design of the distribution network. In order to avoid exceeding the grid's technical restrictions, it is crucial to find the maximum feasible deployment level before rolling out an FWC network. This study provides a method for assessing how FWC technologies influence distribution networks. Using a realistic model of an Indian MV distribution feeder, the proposed methodology is used, providing both quantitative and qualitative results that serve as potential directions for future research studies.

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework’s effectiveness, and the results demonstrate its proficiency in predicting the EVs’ plugged-in status, with XGBoost’s classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs’ power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape.

The electrification of transport is expected to progressively replace significant shares of light duty mobility, especially in large cities. The European Alternative Fuel Infrastructure Regulation (AFIR) aims to drive the adoption of electric mobility by establishing specific targets for charging point deployment. Innovative charging concepts may complement and accelerate the uptake of this fundamental part of the urban mobility transition. In this paper, one such innovative concept is described and its potential impact is assessed. The core idea involves integrating charging points into existing city tramway infrastructures. Turin’s tramway network is taken as a representative case study. The proposed technical solution encompasses a charging hub powered by four isolated DC/DC converters of 50 kW, directly connected to the DC tramway distribution line. Three of these constitute the heart of a 150 kW charger, while the fourth acts as voltage regulator. This native DC installation greatly simplifies the architecture of the DC chargers. Using a conservative approach, it was estimated that a single recharging station could charge more than 60 vehicles daily. This highly scalable and replicable solution, with the potential for over 100 conversion substations across Italy, would enable the installation of numerous high-power chargers in urban settings. Furthermore, additional benefits could be realized through enhanced recovery of kinetic energy from trams, which is currently dissipated on-board.