Search Results (955)

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

The transition to green energy in the transport sector is becoming a priority in the context of global climate challenges and the European Green Deal. This paper investigates the development of alternative fuelling stations, particularly electric vehicle (EV) charging infrastructure and hydrogen stations, across EU countries with a focus on Poland. It combines a policy and technology overview with a quantitative scientific analysis, offering a multidimensional perspective on green infrastructure deployment. A Pearson correlation analysis reveals significant links between charging station density and both GDP per capita and the share of renewable energy. The study introduces an original Infrastructure Accessibility Index (IAI) to compare infrastructure availability across EU member states and models Poland’s EV charging station demand up to 2030 under multiple growth scenarios. Furthermore, the article provides a comprehensive overview of biofuels, including first-, second-, and third-generation technologies, and highlights recent advances in hydrogen and renewable electricity integration. Emphasis is placed on life cycle considerations, energy source sustainability, and economic implications. The findings support policy development toward zero-emission mobility and the decarbonisation of transport systems, offering recommendations for infrastructure expansion and energy diversification strategies. Full article

This paper proposes a novel dual-functional radar-communication (DFRC) framework that integrates unmanned aerial vehicle (UAV) communications into an integrated sensing and communication (ISAC) system, termed the ISAC-UAV architecture. In this system, the UAV’s mobility is leveraged to simultaneously serve multiple single-antenna uplink users (UEs) and perform radar-based sensing tasks. A key challenge stems from the target position uncertainty due to movement, which impairs matched filtering and beamforming, thereby degrading both uplink reception and sensing performance. Moreover, UAV energy consumption associated with mobility must be considered to ensure energy-efficient operation. We aim to jointly maximize radar sensing accuracy and minimize UAV movement energy over multiple time steps, while maintaining reliable uplink communications. To address this multi-objective optimization, we propose a deep reinforcement learning (DRL) framework based on a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) network. By leveraging historical target trajectory data, the model improves prediction of target positions, enhancing sensing accuracy. The proposed DRL-based approach enables joint optimization of UAV trajectory and uplink power control over time. Extensive simulations validate that our method significantly improves communication quality and sensing performance, while ensuring energy-efficient UAV operation. Comparative results further confirm the model’s adaptability and robustness in dynamic environments, outperforming existing UAV trajectory planning and resource allocation benchmarks. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions. Full article

AlGaN-based high-electron-mobility transistors are critical for next-generation power electronics and radio-frequency applications, yet achieving stable enhancement-mode operation with a high threshold voltage remains a key challenge. In this work, we designed p-AlGaN superlattices with different structures and performed energy band structure simulations using the device simulation software Silvaco. The results demonstrate that thin barrier structures lead to reduced acceptor incorporation, thereby decreasing the number of ionized acceptors, while facilitating vertical hole transport. Superlattice samples with varying periodic thicknesses were grown via metal-organic chemical vapor deposition, and their crystalline quality and electrical properties were characterized. The findings reveal that although gradient-thickness barriers contribute to enhancing hole concentration, the presence of thick barrier layers restricts hole tunneling and induces stronger scattering, ultimately increasing resistivity. In addition, we simulated the structure of the enhancement-mode HEMT with p-AlGaN as the under-gate material. Analysis of its energy band structure and channel carrier concentration indicates that adopting p-AlGaN superlattices as the under-gate material facilitates achieving a higher threshold voltage in enhancement-mode HEMT devices, which is crucial for improving device reliability and reducing power loss in practical applications such as electric vehicles. Full article

The improvement of environmental conditions has become a priority for governments and legislators. New electrified mobility systems are increasingly present in our environment, as they enable the reduction of polluting emissions. Electric vehicles (EVs) are one of the fastest-growing alternatives to date, with exponential growth expected over the next few years. In this article, the various charging modes for EVs are explored, and the risks associated with charging technologies are analysed, particularly for charging systems in high-power DC with Lithium battery energy storage, given their long market deployment and characteristic behaviour. In particular, the Arc Flash (AF) risk present in high-power DC chargers will be studied, involving numerous simulations of the charging process. Subsequently, the Incident Energy (IE) analysis is carried out at different specific points of a commercial high-power ‘Mode 4’ charger. For this purpose, different analysis methods of recognised prestige, such as Doan, Paukert, or Stokes and Oppenlander, are applied, using the latest version of the ETAP^® simulation tool version 22.5.0. This study focuses on quantifying the potential severity (consequences) of an AF event, assuming its occurrence, rather than performing a probabilistic risk assessment according to standard methodologies. The primary objective of this research is to comprehensively quantify the potential consequences for workers involved in the operation, maintenance, repair, and execution of tasks related to EV charging systems. This analysis makes it possible to provide safe working conditions and to choose the appropriate and necessary personal protective equipment (PPE) for each type of operation. It is essential to develop this novel process to quantify the consequences of AF and to protect the end users of EV charging systems. Full article

This study evaluates the feasibility and visibility of electric vehicles (EVs) in Egypt, addressing critical research gaps and proposing actionable strategies to drive adoption. Employing a systematic review of academic, governmental, and industry sources, the paper identifies underexplored areas such as rural–urban adoption disparities, lifecycle assessments of EV batteries, and sociocultural barriers, including gender dynamics and entrenched consumer preferences. Its primary contribution is an interdisciplinary framework that integrates technical aspects, such as grid resilience and climate-related battery degradation, with socioeconomic dimensions, providing a holistic overview of EV feasibility in Egypt tailored to Egypt’s context. Key findings reveal infrastructure limitations, inconsistent policy frameworks, and behavioral skepticism as major hurdles, and highlight the untapped potential of renewable energy integration, particularly through synergies between solar PV generation (e.g., Benban Solar Park) and EV charging infrastructure. Recommendations prioritize policy reforms (e.g., tax incentives, streamlined tariffs), solar-powered charging infrastructure expansion, public awareness campaigns, and local EV manufacturing to stimulate economic growth. The study underscores the urgency of stakeholder collaboration to transform EVs into a mainstream solution, positioning Egypt as a regional leader in sustainable mobility and equitable development. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

To address the challenge of dispatching emergency resources for community residents under extreme ice disaster, this paper proposes an emergency resource dispatch strategy based on pre-disaster prediction and dynamic scheduling. First, the fast Newman algorithm is employed to cluster communities, optimizing the preprocessing of resource scheduling and reducing scheduling costs. Subsequently, mobile energy storage vehicles and mobile water storage vehicles are introduced based on the ice disaster trajectory prediction to enhance the efficiency and accuracy of post-disaster resource supply. A grouped scheduling strategy is adopted to reduce cross-regional resource flow, and the dispatch routes of mobile energy storage and water vehicles are dynamically adjusted based on real-time traffic network conditions. Simulations on the IEEE-33 node system validate the feasibility and advantages of the proposed strategies. The results demonstrate that the grouped dispatch and scheduling strategies increase user satisfaction by 24.73%, average state of charge (SOC) by 30.23%, and water storage by 31.88% compared to global scheduling. These improvements significantly reduce the cost of community energy self-sustainability, enhance the satisfaction of community residents, and ensure system stability across various disaster scenarios. Full article

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article