Search Results (13)

The development of a robust and efficient electric vehicle (EV) charging infrastructure is essential for accelerating the transition to sustainable transportation. This systematic review analyzes recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), we identify key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study reveals a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. We provide a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesize practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions. Full article

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a 6 km by 10 km grid size scenario in Dublin in Ireland so that the best CP distribution is obtained; (2) to use a feedforward neural network (FNNs) model to predict the best parameter weight combinations and the corresponding CPs. CP deployment solutions are classified as successful when an EV is located within 100 m of a CP at the end of a trip. We find that (1) integrating the GEECharge and EV Portacharge algorithms with FNNs optimises the distribution of CPs; (2) the normalised optimal weights for the population density, POIs, and most used road parameters determined by this approach result in approximately 109 CPs being allocated in Dublin; (3) resizing the grid from 6 km by 10 km to 10 km by 6 km and rotating it at an angle of $-{350}^{\circ }$ results in a 5.7% rise in the overall number of CPs in Dublin; (4) reducing the grid cell size from 1 km² to 500 m² reduces the mean distance between CPs and the EVs. This research is vital to city planners as we show that city planners can use readily available data to generate these parameters for urban planning decisions that result in EV CP networks, which have increased efficiency. This will promote EV usage in urban transportation, leading to greater sustainability. Full article

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome. Full article

AIM: In our radiation departments, all patients received psycho-oncological support during RT and during follow-up. Based on the latter, the aim of this retrospective analysis was to evaluate the role of tele-visits and in-person psychological support for cancer patients after RT, and to report a descriptive analysis pointing out the needs of psychosocial intervention in a radiation department during radiation treatment. METHODS: According to our institutional care management, all patients receiving RT were prospectively enrolled to receive charge-free assessment of their cognitive, emotional and physical states and psycho-oncological support during treatment. For the whole population who accepted the psychological support during RT, a descriptive analysis was reported. For all patients who agreed to be followed up by a psycho-oncologist, at the end of RT, a retrospective analysis was conducted to evaluate the differences between tele-consultations (video-call or telephone) and on-site psychological visits. Patients were followed up by on-site psychological visit (Group-OS) or tele-consult (Group-TC) visit. For each group, to evaluate anxiety, depression and distress, the Hospital Anxiety Depression Scale (HADS), Distress Thermometer and Brief COPE (BC) were used. RESULTS: From July 2019 to June 2022, 1145 cases were evaluated during RT with structured psycho-oncological interviews for a median of 3 sessions (range 2–5). During their first psycho-oncological interview, all the 1145 patients experienced the assessment of anxiety, depression and distress levels with the following results: concerning the HADS-A scale, 50% of cases (574 patients) reported a pathological score ≥8; concerning the HADS-D scale, 30% of cases (340 patients) reported a pathological score ≥8, concerning the DT scale, 60% (687 patients) reported a pathological score ≥4. Eighty-two patients were evaluated after RT: 30 in the Group-OS and 52 in the Group-TC. During follow-up, a median of 8 meetings (range 4–28) were performed. Comparing psychological data at baseline (beginning of RT) and at the last follow-up, in the entire population, a significant improvement in terms of HADS-A, global HADS and BC was shown (p 0.04; p 0.05; and p 0.0008, respectively). Compared to baseline, statistically significant differences were observed between the two groups in terms of anxiety in favor of on-site visit: Group-OS reported a better anxiety score compared with Group-TC. In each group, a statistical improvement was observed in BC (p 0.01). CONCLUSION: The study revealed optimal compliance to tele-visit psychological support, even if the anxiety could be better controlled when patients were followed up on-site. However, rigorous research on this topic is needed. Full article

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors’ influence on various horizon settings compared with other baselines. Full article

The electric vehicle (EV) industry has made significant progress but, in many markets, there are still barriers holding back its advancement. A key issue is the anxiety caused to the drivers by the limited range of current EV models and the inadequate access to charging stations in long-distance trips, as is the case on highways. We propose an intuitive multi-criteria approach that optimally places EV charging stations on highways that (partially) lack such points. The approach, which is applied in an iterative fashion to dynamically evaluate the alternatives, considers a set of practical criteria related to the traffic intensity and the relative location of the charging stations with interchanges, major cities, and existing stations, thus supporting decisions in a pragmatic way. The optimal locations are determined by taking into consideration constraints about the EV driving range and installation preferences to improve the operation of the highway while ensuring reasonable cost of investment. The proposed approach is showcased in the Egnatia Motorway, the longest highway in Greece that runs a total of 670 km but currently involves a single EV charging point. Our findings illustrate the utility of the proposed approach and highlight its merits as a decision-support tool. Full article

This study aims to investigate the consumer acceptance of Vehicle-to-Grid (V2G) charging of electric vehicle (EV) drivers. To the best of the authors’ knowledge, this is the first V2G acceptance study that is based on actual users’ experience of V2G charging. A test set up with a V2G charge point at a solar carport was constructed at the Delft University of Technology. Seventeen participants in the study were given access to a V2G-compatible Nissan LEAF and the constructed V2G charging facilities, after which they were interviewed. Clear communication of the impacts of V2G charging cycles on EV batteries, financial compensation covering these impacts, real-time insight on the battery state-of-charge and the ability to set operational parameters through a user-friendly interface were all found to foster acceptance. The main barriers for acceptance were the uncertainty associated with battery state-of-charge, the increased need for planning charging and trips, the increased anxiety about the ability of the vehicle to reach its destination, economic and performance-related effects on the EV’s battery and the restriction of the freedom that users associated with their personal vehicles. The participants were found to be divided across high, conditional and low acceptance of V2G charging. The use of V2G charging over the trial period was found to inform their opinions: tangible factors such as range anxiety and the user interface were given more importance than abstract concepts such as lack of standards that were discussed by users without experience of V2G charging. Our study indicates that V2G charging in its current form is acceptable to a section of current EV users. The discussion provides insights on extending the relevance of our findings across other user groups and over further developments in the field. Full article

The use of electric vehicles (EVs) provides a pathway to sustainable transport, reducing emissions and contributing to net-zero carbon aspirations. However, consumer acceptance has been limited by travel range anxiety and a lack of knowledge about EV technology and its infrastructure. This is especially the case in hot and oil-rich areas such as Kuwait, where transport is predominantly fossil fuel-driven. Studying the effects of high ambient temperature on EV efficiency and range is essential to improve EV performance, increase the user base and promote early adoption to secure more environmental benefits. The ability to determine the energy consumption of electric vehicles (EVs) is not only vital to reduce travel range anxiety but also forms an important foundation for the spatial siting, operation and management of EV charging points in cities and towns. This research presents an analysis of data gathered from more than 3000 journeys of an EV in Kuwait representing typical vehicle usage. The average energy intensity and consumption of the car/kilometre travelled were calculated for each journey, along with ambient temperature measured by the vehicle. The analysis indicates that energy intensity reaches a minimum at a starting temperature between 22 °C and 23 °C. Energy intensity rises with decreasing temperature below this point and with increasing temperature above this point. The results show that many vehicle journeys started with high temperatures, with about half of journeys starting at 30 °C or above and approximately a quarter at 40 °C or above. Fitting a model to the empirical data for trip starting temperature and energy intensity, average efficiency is impacted at high car temperatures, with energy intensity modelled at 30 °C and 40 °C to be higher by 6% and 22%, respectively. These findings have implications for vehicle range, EV charging infrastructure and car storage and parking provision. Full article

The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fuelled by the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that bypasses the limitations of the “reserve now” function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station’s reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations and the occupation of several stations without queues, assuring, thus, a proper distribution of the available energy resources, while increasing end-user satisfaction. Our solution is independent from the OCPP reservation method; therefore, the authentication and reservation processes performed by the proposed algorithm run only through the central system, authorizing only the creator of the reservation to start the charging transaction. Full article

For electric vehicles (EVs) to realise the UK government’s goal of mass-market dominance, there are surmountable hurdles to resolve before car users accept this radical shift in motoring technology. This study focuses on recent EV adopters who experience a new phenomenon described as charge point trauma (CPT). In contrast to range anxiety, we define CPT as the psychological, physiological, and behavioural condition where EV user’s experiences develop trauma or anxiety in response to the availability of sufficient charge points, locations, payment processes, and operability. Resolving impediments to EV usage reduces long-term growth barriers, which we argue can subsequently lower or even eliminate EV driver anxiety. We conclude that range anxiety still plays a major part in overall EV driver trauma, and after deep analysis of our case study data conclude that a trauma other than range anxiety exists at the charge point. To mitigate this phenomenon, we propose a regulatory framework comprising a series of stimuli to encourage EV uptake. These recommendations should be targeted at regulating a new generation of EV charging stations to meet operational parity with current fossil fuel filling stations by ensuring they are always on, available in sufficient numbers, accessible and operable as part of the UK motorway and major trunk network. This will de-risk EV purchasing and stimulate their adoption in this embryonic stage, reducing CPT in the process. Full article

Standardisation is fundamental to ensuring that new technologies develop and grow unhindered by manufacturer-led standards. Dismissing this vital issue can have a detrimental effect on society regarding adopting new technologies, particularly when government targets and regulations are crucial for their success. We have witnessed competing global industries struggle for dominance, such as Betamax versus VHS, where each had a similar user outcome, but the confusion of differing formats slowed growth. We analyse emerging standards for electric vehicle rapid charging and investigate how standardisation challenges affect stakeholders by reviewing the existing literature on single-mode and polymodal harmonisation. By assimilating existing evidence, we then develop a new understanding of the science behind multi-model standardisation (MMS) approaches. Our literature review reveals three primary standardisation issues: (1) charge connections, (2) car to charger communication protocols, and (3) charge payment methods. We then analyse each mode type’s benefit, observing how each example contributes to the overall outcome, and suggest that their impact depends on car to charger handshake timing and intuitive user interaction. Using a structured survey of 282 respondents, we analyse end-user satisfaction for factors affecting growth in the EV sector and compare these findings with the factors identified during our literature review. We consequently articulate a programme for future research to understand EV rapid charger standardisation better, proposing recommendations for vested stakeholders that embrace sponsors in societal, technological and scientific transformation. Full article

Development of electromobility in urban areas requires an appropriate level of vehicle charging infrastructure. Numerous methods for siting of charging stations have been developed to date, and they appear to be delivering diverse outcomes for the same area, which is why local authorities face the problem of choosing the right station layout. The solution proposed in this article is to use a travel planner to evaluate the distribution of charging stations over the area of a metropolis. The decision making support is achieved by determining optimal travel routes for electric vehicles according to their initial state of charge for the three selected station siting methods. The evaluation focused on the following three aspects: (1) number of travels that cannot be made (due to the lack of a charging station at a certain distance around the start point), (2) extension of the travel caused by the need to recharge the vehicle on-route, and (3) additional energy consumption by electric vehicles required to reach the charging station (necessity of departing from the optimal route). An analysis of the results has made it possible to determine a solution which is superior to others. For the case study analysed in the paper, i.e., the territory of the Metropolis of Upper Silesia and Dabrową Basin (Górnośląsko-Zagłębiowska Metropolia, GZM), the distribution of charging stations established in line with method I has returned the best results. What the method in question also makes possible is to indicate a safe minimum energy reserve to complete the travel by eliminating situations of unexpected vehicle immobilisation due to on-route energy depletion and by minimising the phenomenon referred to as range anxiety. Full article

The new generation of fast charging systems faces a formidable technological challenge, aiming to drastically reduce the time needed to recharge an electric vehicle as a way to tackle the range anxiety issue. To achieve this, high power (up to 350 kW) is transferred from the grid to the vehicle, leading to potentially high values of low frequency magnetic fields. This study presents the results of measurements of magnetic flux density (B-field) emitted by two different high power charging systems. The electric vehicle used for the recharge was able to digest up to 83 kW of delivered power. The test procedure was designed to identify the locations where the maximum B-field levels were recorded and to measure the exposure indices according to reference levels for general public exposure defined in the Council Recommendation 1999/519/EC. Measurements in close proximity to the power cabinets during the recharge revealed that, at some points, exposure indices were higher than 100%, leading to the identification of a distance from the system components at which the value was lower than the reference level. In the worst case, this distance was 31 cm. Full article