Search Results (286)

This paper presents a systematic review of energy management strategies (EMSs) for fuel cell hybrid unmanned aerial vehicles (UAVs). It begins by explaining the necessity of hybrid energy systems. This paper then categorizes existing EMSs into three main classes: rule-based, optimization-based, and learning-based. It provides an in-depth analysis of the core principles, technical advantages, and application challenges for each class. The review also traces the evolution of these strategies from experience-dependent methods to data-driven and autonomous learning approaches. A key finding is that future EMSs will not operate as standalone control modules. By addressing the limitations of current studies, this paper identifies four key development trends: multi-objective collaborative optimization, joint energy-task planning, safe deployment from simulation to real-world environments, and high-fidelity dynamic validation. This work aims to offer theoretical guidance and technological foresight for the research and development of next-generation, high-performance, and high-reliability hydrogen-powered UAVs. Full article

In accordance with the European common rules for the internal electricity market, suppliers offer end users contracts with dynamic energy prices. To reduce energy costs, prosumers must manage their installations with energy storage devices (ESSs). The authors propose a novel control strategy with a relatively simple simulation-based algorithm that effectively reduces daily energy costs by managing the ESS charging and discharging schedule under different types of dynamic energy tariffs. The algorithm operates in a running window mode to ensure ongoing control updates in response to the changing conditions of the prosumer’s installation operation and dynamically changing energy prices. A feature of the control system is its ability to regulate the power exchanged with the supply network in response to an external signal from a superior control system or a network operator. This feature allows the control system to participate in regulatory services provided by the prosumer to the DSO. The effectiveness of the proposed control algorithm was verified in the PSCAD V4 Professional environment and with the MS Excel SOLVER for Office 365 optimisation tool. The results showed good accuracy with respect to the cost reduction algorithm and confirmed that the additional regulatory service can be effectively implemented within the same prosumer ESS control system. Full article

Energy communities represent an important step towards clean energy; however, their management is a complex task due to various factors such as fluctuating demand and energy prices, variable renewable generation, and external factors such as power outages. This paper investigates the effectiveness of a Reinforcement Learning agent, based on the Proximal Policy Optimisation (PPO) algorithm, for energy management across three different energy community configurations. The performance of the PPO agent is compared against a Rule-Based Controller (RBC) and a baseline scenario using solar generation but no active management. Simulations were run in the CityLearn framework to simulate real world data. Across the three evaluated community configurations, the PPO agent achieved its greatest improvement over a single run in the scenario where all participants were prosumers (Schema 3), with a reduction of 9.2% in annual costs and carbon emissions. The main contribution of this work is demonstrating the viability of Reinforcement Learning agents in energy optimization problems, providing an alternative to traditional RBCs for energy communities. Full article

This study proposes an edge-centric outdoor parking management system that performs on-site inference on a low-power embedded device and outputs slot-level occupancy decisions in real time. A dataset comprising 13,691 images was constructed using two cameras capturing frames every 3–5 s under diverse weather and illumination conditions, and a YOLOv8-based detector was trained for vehicle recognition. Beyond raw detections, a temporal occupancy decision module is introduced to map detections to predefined slot regions of interest (ROIs) while applying temporal smoothing and occlusion-robust rules, thereby improving stability under rainy and nighttime conditions. When deployed on an AI-BOX edge platform, the proposed system achieves end-to-end latency p50/p95 of 195 ms and 400 ms, respectively, while sustaining 10 FPS at 3.35 W (2.99 FPS/W) during continuous 24-hour operation. Compared with conventional sensor-based architectures, the proposed design significantly reduces upfront deployment costs and recurring maintenance requirements. Furthermore, when integrated with dynamic pricing mechanisms, it enables accurate and automated fee calculation based on real-time occupancy data. Overall, the results demonstrate that the proposed approach provides a flexible, scalable, and cost-efficient foundation for next-generation smart parking infrastructure. Full article

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM. Full article

The usage of Artificial Intelligence (AI) in control loops and rule-based frameworks is a novel approach in automation and decision-making processes. Large Language Models (LLMs) are redefining conventional rule-based systems by introducing intuitive natural language interfaces, drastically changing the creation of rules, and minimizing operational complexity. Unlike static controllers, AI-enhanced systems can autonomously evolve with real-time environmental changes, achieving optimal performance without manual intervention. By allowing non-experts to modify rules through natural language commands, LLM can change the control system management. These advancements not only improve adaptability and operational efficiency but also reduce downtime through proactive error detection and self-correction mechanisms. AI-powered systems allow refining operations, thus accelerating response speeds and increasing reliability. The synergy between rule-based logic and AI-driven intelligence provides a new approach for autonomous systems, improving their capability of context-specific decision-making. In this paper, an approach is presented to control a storage system by natural language commands. The comparison of the Hungarian and English language interpretations is discussed. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

As a fundamental element in industrial fluid transportation, the main pump fulfills an irreplaceable function in critical infrastructure, including the energy, water conservancy, petrochemical, and sewage treatment industries. As the core component of key power equipment, its operating condition is intrinsically connected to the safety, stability, and reliability of the entire system. This paper provides a systematic review of the latest advances in fault mechanism analysis and diagnosis methods for main pump systems. First, the typical structural composition and functional characteristics of the main pump system are examined, and the occurrence mechanisms and evolution rules of typical faults, such as mechanical malfunctions and performance degradation caused by hydraulic imbalance, are discussed in detail. Second, the main technical approaches to fault diagnosis are summarized and reviewed, including diagnosis methods based on signal processing, modeling, data-driven techniques, and multi-source information fusion. The advantages, limitations, and application scopes of these approaches are comparatively analyzed. On this basis, the development trends in main pump fault diagnosis technology and the key challenges faced—such as strong noise, small sample size, and multiple fault coupling—are identified and discussed. Finally, future research prospects are put forward in view of the limitations of current research. This review aims to provide theoretical insights and technical support for advancing condition monitoring, fault diagnosis, and health management of main pump systems. Full article

This research explores how coalitions of leaders who score high in the Dark Triad traits—narcissism, Machiavellianism, and psychopathy—rebuild moral architectures in organizations to consolidate power, suppress dissent, and secure their rule. Contrary to work that has focused predominantly on individual toxic leaders, this research examines the collective processes that emerge when multiple high-DT-scoring leaders coalesce and unify their moral leadership front. Adopting a qualitative, article-based document analysis methodology, this study synthesizes and critiques evidence from 55 peer-reviewed articles published between 2015 and 2025. Thematic analysis identified three fundamental dynamics through which Dark Triad leaders collectively exercise dominance. The first, the Ethics Cartel, involves the construction of a shared moral façade that legitimates power and shields wrongdoing. The second, Mutual Cover, outlines forms of mutual protection in which leaders shield one another from accountability and scrutiny. The third, Cultural Capture, outlines processes through which organizational culture is increasingly reconfigured such that “ethics” are structured to favor leadership over employees or wider stakeholders. This study illustrates how these coalitions cross over into individual transgressions, creating systemic risk that warps the fabric of organizational culture. Employees are confronted with a work culture that positions ethics as a means of developing survival adaptive mechanisms, such as silence, withdrawal, or compliance. These processes not only harm psychological safety and break trust but also disable accountability mechanisms established to maintain integrity. This study contributes to the study of leadership and organizational ethics by framing ethics not as merely an individual moral stance but as a collective instrument of power. It calls for more attention to the risks that follow collaboration among toxic leaders and for governance arrangements that address the organizational and systemic consequences of these unions. By situating these findings within the broader debate on power, people, and performance, this paper aligns with the focus of the Special Issue “Power, People, and Performance: Rethinking Organizational Leadership and Management” by showing how collective Dark Triad leadership distorts organizational performance outcomes while reshaping power relations in ways that undermine people’s trust and well-being. These insights extend Alowais & Suliman’s findings, highlighting the systemic feedback loops sustaining ethical distortion. Full article

The maintenance of power distribution lines is critically challenged by vegetation encroachment, posing significant risks to the reliability and safety of power utilities. Traditional manual inspection methods are resource-intensive and lack the precision required for effective and proactive maintenance. This paper presents an automated, accurate, and efficient approach to vegetation management near power lines by leveraging advancements in LiDAR as a remote sensing technology and deep learning algorithms. The RandLA-Net model is employed for semantic segmentation of large-scale point clouds to accurately identify vegetation, poles, and power lines. A comprehensive sensitivity analysis is conducted to optimize the model’s hyperparameters, enhancing segmentation accuracy. Post-processing techniques, including clustering and rule-based thresholding, are applied to refine the semantic segmentation results. Proximity detection is applied using spatial queries based on a KDTree structure to assess potential risks of vegetation near power lines. Furthermore, a digital twin of the power distribution network and surrounding trees is developed by integrating 3D object registration and surface generation, enriching it with semantic attributes and incorporating it into City Information Modeling (CIM) systems. This framework demonstrates the potential of remote sensing data integration for efficient environmental monitoring in urban infrastructure. The results of the case study on the Toronto-3D dataset demonstrate the computational efficiency and accuracy of the proposed method, presenting a promising solution for power utilities in proactive vegetation management and infrastructure planning. The optimized full 9-class model achieved an overall accuracy of 96.90% and IoU scores of 97.05% for vegetation, 88.09% for power lines, and 82.33% for poles, supporting comprehensive digital twin creation. An auxiliary 4-class model further improved targeted performance, with IoUs of 99.55% for vegetation, 88.79% for poles, and 87.18% for power lines. Full article

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility. Full article

Power transformers are critical assets in modern power grids, where failures can lead to significant operational disruptions and financial losses. Dissolved Gas Analysis (DGA) is a key sensor-based technique widely used for condition monitoring, but traditional diagnostic approaches rely on deterministic thresholds that overlook uncertainty in degradation dynamics. This paper proposes a probabilistic framework for Prognostics and Health Management (PHM) of power transformers, integrating self-adaptive Auto Regressive Integrated Moving Average modeling with a probabilistic reformulation of Duval’s graphical methods. The framework enables automated estimation of fault types and failure likelihood directly from DGA sensor data, without requiring labeled datasets or expert-defined rules. Dissolved gas dynamics are forecasted using time-series models with residual-based uncertainty quantification, allowing probabilistic fault inference from predicted gas trends without assuming deterministic persistence of a specific fault type. A sequential pipeline is developed for real-time fault tracking and reliability assessment, aligned with IEC, IEEE, and CIGRE standards. Two case studies validate the method: one involving gas loss in an experimental setup and another examining thermal degradation in a 345 kV transformer. Results show that the framework improves diagnostic reliability, supports early fault detection, and enhances predictive maintenance strategies. By combining probabilistic modeling, time-series forecasting, and sensor-based diagnostic inference, this work contributes a practical and interpretable PHM solution for sensor-enabled monitoring environments in modern power grids. Full article

Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM predictor was proposed to improve the velocity and road gradient prediction accuracy, thus leading an accurate power demand for EMS and enabling real-time parameter adaptation, especially in the nonlinear area. Second, the model predictive controller (MPC) was adopted to construct the EMS by solving a multi-objective problem using quadratic programming. Third, a combination of rule-based and fuzzy logic-based strategies was introduced to adjust the weights and constraints, optimizing UC utilization while alleviating the burden on batteries. Simulation results show that the proposed scheme boosts UC utilization by 10.98% and extends battery life by 19.75% compared to traditional MPC. These gains underscore the practical viability of intelligent, optimizing EMSs for HESSs. Full article

Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based tool for the dynamic simulation of fuel cell–battery hybrid powertrains. The model integrates train dynamics, rule-based energy management, system efficiencies, and component degradation, enabling both energy and cost analyses over the vehicle’s lifetime. The objective is to assess the techno-economic performance of different powertrain configurations. Sensitivity analyses were carried out by varying two sizing parameters: the nominal power of the fuel cell (parameter m) and the total battery capacity (parameter n), across multiple real-world railway routes. Results show a slight reduction in lifecycle costs as m increases (5.1 €/km for m = 0.50) mainly due to a lower FC degradation. Conversely, increasing battery capacity (n) lowers costs by reducing cycling stress for both battery and FC, from 5.3 €/km (n = 0.10) to 4.5 €/km (n = 0.20). In general, lowest values of m and n provide unviable solutions as the battery discharges completely before the end of the journey. The study highlights the critical impact of the operational profile: for a fixed powertrain configuration (m = 0.45, n = 0.20), the specific cost dramatically increases from 4.44 €/km on a long, flat route to 15.8 €/km on a hilly line and up to 76.7 €/km on a mountainous route, primarily due to severe fuel cell degradation under transient loads. These findings demonstrate that an “all-purpose” train sizing approach is inadequate, confirming the necessity of route-specific powertrain optimization to balance techno-economic performance. Full article

We present an experimentally validated, engineering-oriented framework for the design and operation of pin-to-water (PTW) atmospheric discharges to produce hydrogen peroxide (H₂O₂) on demand. Motivated by industrial needs for safe, point-of-use oxidant supply, we combine time-resolved diagnostics (FTIR, OES), liquid-phase analysis (ion chromatography, pH, conductivity), and coupled plasma-chemistry/fluid simulations to link plasma state to aqueous H₂O₂ yield. Under the tested conditions (14.3 kHz, 0.2 kW; electrode to quartz wall distance 12–14 mm; coolant setpoints 0–40 °C), H₂O₂ concentration follows a reproducible non-monotonic trajectory: rapid accumulation during the early treatment (typical peak at ~15–25 min), followed by decline with continued operation. The decline coincides with a robust vibrational-temperature (T_vib) threshold near ~4900 K measured from N₂ emission, and with concurrent NO_X accumulation and bulk acidification. Global chemistry modeling and Fluent flow fields reproduce the observed trend and show that both vibrational excitation (kinetics) and convective transport (mass/heat transfer) determine the productive time window. Based on these results, we formulate practical design rules—electrode gap (power density), discharge current control, thermal/flow management, water quality, and OES-based T_vib monitoring with an automated stop rule—that maximize H₂O₂ yield while avoiding NO_X-dominated suppression. The study provides a clear path for transforming mechanistic plasma insights into deployable, industrial H₂O₂ generator designs. Full article