Search Results (19)

This study examines Connecticut’s strategic approach to electric vehicle (EV) workforce development within the framework of the Bipartisan Infrastructure Law (BIL) and its National Electric Vehicle Infrastructure (NEVI) program. Amid the U.S. goal to transition to a zero-emission vehicle fleet by 2050, this research investigates whether Connecticut’s current policies sufficiently address the need to reskill automotive mechanics into qualified EV technicians. Using a qualitative case study methodology, semi-structured interviews were conducted with state workforce representatives and analyzed through inductive coding within Kotter’s 8-Step Change Model. Findings reveal that while Connecticut aligns with federal NEVI goals for infrastructure, it lacks a dedicated budget and clearly defined pathways for technician training. Stakeholder collaboration remains fragmented, and efforts to empower workforce transformation are in the early stages. The study concludes that Connecticut risks falling behind unless it integrates a robust workforce development strategy that includes cross-sector partnerships, pilot training programs, and transparent certification pathways. These findings highlight the importance of aligning state-level EV infrastructure planning with human capital development and offer actionable insights for other states navigating similar transitions. Full article

This paper presents experimental testing results which describe the response of modern residential loads and electric vehicle (EV) chargers to various voltage magnitude, frequency, and phase angle disturbances. The purpose of these tests is to replicate real life network conditions and assist Network Service Providers and the Australian Energy Market Operator in identifying and predicting potential power variation and system stability issues caused by load behaviour during power system transient phenomena. By examining the behaviour of typical loads connected to distribution networks, a deeper understanding of their response can be achieved, enabling the refinement of composite load models that are compatible with the Western Electricity Coordinating Council dynamic composite load model (CMPLDW) structure presently used for dynamic studies. The performance of a wide range of common appliances found in residential settings, such as refrigerators, microwave ovens, air conditioners, direct-on-line motor-based appliances, and EV chargers, has been evaluated. The results obtained from these tests offer valuable insights into the behaviour of different load types and illustrate differing performances from established model parameters, identifying the need to refine existing CMPLDW models. The results also support the reclassification of several appliances within the composite load model, motivate the introduction of a dedicated EV charger component, and empower network operators to improve the modelling of modern power network responses. Full article

The rapid growth of electric vehicles (EVs) and energy storage systems (ESSs) has accelerated the generation of waste lithium-ion batteries, posing both environmental and industrial challenges. This study proposes and experimentally validates an AI-based Waste Battery and Plasma Convergence System (AI-WBPCS) designed to integrate residual energy recovery from retired EV batteries with adaptive plasma control. The system aims to establish a self-optimizing energy reuse framework that enhances real-time energy utilization, improves plasma process stability, and supports sustainable circular energy ecosystems. The AI-WBPCS consists of three key sub-models: D₁ for plasma output prediction, D₂ for battery health evaluation, and D₃ for adaptive energy-matching control. These models operate synergistically under a hybrid STM32–Jetson Nano platform, enabling predictive analysis and closed-loop optimization. Experimental validation using 2P6S retired EV modules demonstrated that the D₂ model achieved a 93.7% SOH prediction accuracy and a 2.3% mean absolute error (MAE) in DCIR estimation. The AI-controlled plasma subsystem maintained output stability within ±2.1%, compared to fluctuations exceeding 6% under conventional rule-based methods. The overall energy-matching efficiency (η) reached 96.5%, representing a 13% improvement in power coordination performance. Interpretability analysis using SHAP (SHapley Additive exPlanations) identified SOH (46%) and DCIR (29%) as the dominant features influencing AI-driven decisions, confirming the physical relevance and transparency of the model. The AI-WBPCS provides a practical pathway toward circular-economy-oriented energy reuse, enabling intelligent, autonomous plasma systems for applications such as smart agriculture, biomedical sterilization, and decentralized wastewater treatment. Overall, this research establishes a new paradigm for AI-empowered electrochemical–plasma systems, where artificial intelligence not only enhances operational efficiency but also redefines end-of-life batteries as adaptive energy resources for next-generation green technologies. Full article

This paper aims to provide small- and medium-sized enterprises (SMEs) owned by families with a simple, achievable technical methodology for the assessment of sustainable mobility alternatives, in particular, the purchase of electric vehicles (EVs) and photovoltaic (PV) systems. By adopting a comprehensive comparative analysis approach, this research aims to empower SMEs to make highly informed decisions concerning the choice of vehicles and energy systems that provide strategic and sustainable value. Based on a quantitative analysis linked to the total costs over ten years, and considering the different types of vehicles (electric, hybrid, and combustion) and the integration of PV systems, practical formulas are used to calculate the total cost of ownership (TCO), energy consumption, and CO₂ emissions. The results show that adopting electric vehicles, especially those complemented by photovoltaic systems with storage for night-time charging, can significantly reduce operating costs and carbon emissions, generating economic and environmental value. This study provides an accessible and applicable approach to the context of family SMEs, facilitating the analysis and choice of mobility options based on simple and commercially available data. By focusing on value creation through informed and strategic decisions, this work offers a relevant contribution to the competitiveness and sustainability of SMEs, promoting the adoption of sustainable mobility technologies in an integrated and effective manner. Full article

Recent years have witnessed an unprecedented boom of Electric Vehicles (EVs). However, EVs’ further development confronts critical bottlenecks due to EV Energy (EVE) issues like battery hazards, range anxiety, and charging inefficiency. Emerging data-driven EVE Management (EVEM) is a promising solution but still faces fundamental challenges, especially in terms of reliability and efficiency. This article presents iEVEM, the first big data-empowered intelligent EVEM framework, providing systematic support to the essential driver-, enterprise-, and social-level intelligent EVEM applications. Particularly, a layered data architecture from heterogeneous EVE data management to knowledge-enhanced intelligent solution design is provided, and an edge–cloud collaborative architecture for the networked system is proposed for reliable and efficient EVEM, respectively. We conducted a proof-of-concept case study on a typical EVEM task (i.e., EV energy consumption outlier detection) using real driving data from 4000+ EVs within three months. The experimental results show that iEVEM achieves a significant boost in reliability and efficiency (i.e., up to 47.48% higher in detection accuracy and at least 3.07× faster in response speed compared with the state-of-art approaches). As the first intelligent EVEM framework, iEVEM is expected to inspire more intelligent energy management applications exploiting skyrocketing EV big data. Full article

The study examines the effects of semiconductor scarcity on the electric vehicle (EV) supply chain caused by an increase in electronics demand after the 2020 automobile industry downturn due to the COVID-19 pandemic. The rising demand for semiconductor chips in the automotive industry, especially in EVs, necessitates strategic measures for original equipment manufacturers and suppliers to strengthen supply chain resilience. This study uses a consequence-based decision-making framework that uses a hybrid Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with Interpretive Structure Modeling (ISM). By leveraging this innovative approach, the research unveils complex causal relationships among supply chain strategies, providing quantifiable insights for prioritizing resilience in the face of multifaceted risks such as trade wars, regulatory changes, and raw material shortages. In addition, the study enhances our comprehension of supply chain resilience within the electric vehicle sector by illuminating aspects that have not been thoroughly examined by the Multi-Criteria Decision Analysis (MCDA) technique employed in this research. The analysis includes the adoption of multisourcing, fostering ecosystem partnerships, and improving supply chain visibility. Through these novel insights, this analysis aims to empower stakeholders and small- to medium-sized enterprises to navigate future automotive market dynamics, with a focus on evolving manufacturer–supplier relationships in the midst of technological advancements. Full article

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. Full article

In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations. Full article

In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users’ behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid. Full article

In light of COVID-19, people are increasingly anxious about indoor air quality data in places where they live and work. Access to this data using a consumer-grade air quality monitor has become a way of giving agency to building users so that they can understand the ventilation effectiveness of the spaces where they spend their time. Methods: Fourteen low-cost, air quality devices marketed to consumers were tested (seven types, two of each product): AirBird, Airthings View Plus, Aranet4 Home, Awair Omni, Eve Room, Laser Egg + CO₂, and Purple Air PA-1. The study focus was accuracy and useability using three methods: a low-cost laboratory setting to test accuracy for CO₂; a comparison to a calibrated, research grade meter for particulate matter (PM2.5), temperature, and relative humidity; and short-term field testing in a residential environment to understand the quality of feedback given to users. Results: Relating to accuracy, all devices were within acceptable ranges for temperature, relative humidity, and CO₂, and only one brand’s results met the accuracy threshold with the research grade monitor when testing PM2.5. In terms of usability, a significant variation in response time and data visualization was found on the devices or in the smartphone applications. Conclusions: While accuracy in IAQ data is important, in low-cost air quality devices marketed to consumers it is just as important that the data be presented in a way that can be used to empower people to make decisions and modify their indoor environment. We concluded that response time, user-interface, data sharing, and visualization are important parameters that may be overlooked if a study just focuses on accuracy. The design of the device, including its appearance, size, portability, screen brightness, and sound or light warning, must also be considered. The act of measuring is important, and more studies should focus on how users interpret and react to building performance data. Full article

In an increasingly competitive landscape, both researchers and businesses are showing growing interest in promoting employee’s innovative work behavior (EIWB). Although earlier studies have highlighted the significance of transformational leadership (TL) in cultivating innovation among employees, there needs to be more understanding regarding the precise mechanisms and processes by which leaders exert their influence over the IWB of their employees. This study is based on the social exchange theory (SET) and upper echelon theory (UET) to investigate how the relationship between TL and employees’ IWB is mediated by the employees’ intellectual agility (EIA) and the employee’s voice (EV). To the best of researchers’ knowledge, this study represents the pioneering effort to examine the mediating mechanisms of EIA and EV between TL and EIWB within the specific context of small and medium Enterprises (SMEs) in a developing country. An online self-administered questionnaire was utilized to collect data from 430 SMEs in Pakistan. The proposed hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The study findings revealed a significant influence of TL on EIWB mediated by both their EIA and EV. These findings empower leaders to recognize their pivotal roles in nurturing innovation within their enterprises and crafting an optimal culture and climate conducive to innovative endeavors. Furthermore, this insight enables leaders to establish innovative environments that promote employees’ confident sharing of ideas and concepts. The study also includes a comprehensive finding and their implications, limitations, and suggestions for future research directions. Full article

Regarding the importance of the Internet of Things (IoT) and the Metaverse as two practical emerging technologies to enhance the digitalization of public transportation systems, this article introduces an approach for the improvement of IoT and unmanned electric vehicles in the Metaverse, called the Internet of Unmanned Electric Vehicles (IoUEVs). This research includes two important contributions. The first contribution is the description of a framework for how unmanned electric vehicles can be used in the Metaverse, and the second contribution is the creation of a digital twin for an unmanned electric vehicle. In the digital twin section, which is the focus of this research, we present a digital twin of an electronic differential system (EDS) in which the stability has been improved. Robust fuzzy logic algorithm-based speed controllers are employed in the EDS to independently control the EV wheels driven by high-performance brushless DC (BLDC) electric motors. In this study, the rotor position information of the motors, which is estimated from the low-precision Hall-effect sensors mounted on the motors’ shafts, is combined and converted to a set of common switching signals for empowering the EDS of the electric vehicle traction drive system. The proposed digital twin EDS relies on an accurate Hall sensor signals-based synchronizing/locking strategy with a dynamic steering pattern capable of running in severe road conditions with different surface profiles to ensure the EV’s stability. Unlike recent EDSs, the proposed digital twinning approach includes a simple practical topology with no need for auxiliary infrastructures, which is able to reduce mechanical losses and stresses and can be adapted to IoUEVs more effectively. Full article

Extracellular vesicles (EVs) are important mediators of intercellular communication in several physiopathological conditions. Oxidative stress alters EVs release and cargo composition depending on the cell type and stimulus. Recently, most of the EVs studies have focused on the characterization of their cargo, rather than on the morphological features (i.e., size distribution, shape, and localization on the cell surface). Due to their high heterogeneity, to fully characterize EVs both the functional and morphological characterization are required. Atomic force microscopy (AFM), introduced for cell morphological studies at the nanoscale, represents a promising method to characterize in detail EVs morphology, dynamics along the cell surface, and its variations reflecting the cell physiological status. In the present study, untreated or H₂O₂-treated wild-type and SOD1-G93A SH-SY5Y cells have been compared performing a transmission electron microscopy (TEM) and AFM morpho-quantitative analysis of budding and released vesicles. Intriguingly, our analysis revealed a differential EVs profiling, with an opposite behavior and implying different cell areas between WT and SOD1-G93A cells, on both physiological conditions and after H₂O₂ exposure. Our results empower the relationship between the morphological features and functional role, further proving the efficacy of EM/AFM in giving an overview of the cell physiology related to EVs trafficking. Full article

Modern communication networks and digital control techniques are used in a smart grid. The first step is to classify the features of several communication networks and conduct a comparative investigation of the communication networks applicable to the smart grid. The integration of distributed generation has significantly increased as the global energy demand rises, and sustainable energy for electric vehicles and renewable energies worldwide are being pursued. Additional explanations for this surge include environmental concerns, the reforming of the power sector, and the advancing of small-scale electricity generation technologies. Smart monitoring and control of interconnected systems are required to successfully integrate distributed generation into an existing conventional power system. Electric-vehicles-based smart grid technologies are capable of playing this part. Smart grids are crucial to avoid becoming locked in an obsolete energy infrastructure and to draw in new investment sources and build an effective and adaptable grid system. To achieve reliability and high-quality power systems, it is also necessary to apply intelligent grid technologies at the bulk power generation and transmission levels. This paper presents smart grid applicable communication networks and electric vehicles empowering distributed generation systems. Additionally, we address some constraints and challenges and make recommendations that will give proper guidelines for academicians and researchers to resolve the current issues. Full article

Multiple vaccines may prevent meningitis and encephalitis (M/E). In China, the meningococcal vaccine and Japanese encephalitis vaccine (JEV) have been included in the expanded program of immunization (EPI). The pneumococcal vaccine, Haemophilus influenzae type b (Hib) vaccine, rotavirus vaccine, and enterovirus 71 (EV-71) vaccine are non-EPI vaccines and are self-paid. We aim to investigate the uptake of these M/E vaccines in children and the related knowledge and health beliefs among family caregivers. A total of 1011 family caregivers with children aged 1–6 years in Shanghai, China were included in the study. The uptake of the pneumococcal vaccine, Hib-containing vaccine, rotavirus vaccine, and EV-71 vaccine remained at 44.0–48.1% in children, compared with the higher uptake of the meningococcal vaccine (88.8%) and JEV (87.1%). Moreover, family caregivers had limited knowledge on the M/E pathogens and possible vaccines. Their health beliefs were moderate to high. Then, a health belief model (HBM) and a structural equation model were established. The uptake of four non-EPI vaccines was significantly influenced by family income (β = 0.159), knowledge (β = 0.354), self-efficacy (β = 0.584), and perceived susceptibility (β = 0.212) within an HBM. Therefore, it warrants further improving the uptake rate for these non-EPI vaccines to prevent potential M/E in children. A specific health promotion may empower the caregivers’ decision-making on childhood vaccination. Full article