Search Results (335)

Electric vehicle (EV) usage has increased significantly in the past few years, which may create challenges for distribution system operators due to EV charging needs. In this paper, we propose an approach for planning and operating EV charging stations, considering the challenges that distribution networks may face. A two-step framework is proposed in this paper. First, the optimal size and location of a charging station is determined using a multi-objective optimization problem considering minimizing power losses and voltage drop while maximizing load placements. Then, an optimal scheduling scheme is employed to charge and discharge the vehicles on the selected buses. Simulation studies were conducted using IEEE 33- and 123-bus systems; the results show that the proposed framework significantly enhances the buses’ voltages and line power flows. In order to plan for charging stations, several factors need to be considered, such as optimal size and location, the daily load curve for the given system, the time of use (TOU), and the charging patterns of EV owners. Without careful planning and operation, the system may suffer vulnerability and line overloading, which may lead, eventually, to cascading outages and interruptions. By improving grid utilization, reducing losses, and enabling coordinated EV charging and discharging, the proposed framework supports more sustainable energy use and facilitates the integration of electric mobility into future low-carbon power systems. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

This study presents the prototype manufacturing and experimental validation of a 1 kW, 750 rpm three-phase outer-rotor permanent magnet-assisted synchronous reluctance motor (PMASynRM) designed for in-wheel electric vehicle applications. The work is based on a previously reported electromagnetic design and finite element method (FEM)-based optimization framework and focuses on the physical implementation and experimental evaluation of the proposed motor. The prototype was manufactured using M470-50A grade electrical steel laminations and arc-shaped N35H NdFeB permanent magnets embedded within a three-barrier transversally laminated anisotropic rotor structure. A custom-built experimental test bench consisting of the PMASynRM prototype, a PMSM generator with a controllable resistive load bank, a torque transducer, and a precision power analyzer was developed to evaluate motor performance under controlled operating conditions. Experimental investigations were carried out under four steady-state load conditions—no-load, 13 Nm, 20 Nm, and 26 Nm—as well as during dynamic stepwise load transitions representative of in-wheel drive operation. The measured results show good agreement with FEM predictions, with a maximum efficiency of 90.55% at nominal load and efficiency values remaining above 87% under overload conditions up to 26 Nm. Minor differences between simulation and experimental results are mainly associated with mechanical friction, bearing losses, and manufacturing tolerances that are not fully captured in the numerical model. The study provides experimental validation of an outer-rotor PMASynRM prototype under multi-load steady-state and dynamic operating conditions for in-wheel electric vehicle applications. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

This paper proposes a finite-time adaptive backstepping active suspension control strategy, integrating command filtering and composite learning, to address the degradation of ride comfort and attitude stability in heavy-duty vehicles caused by shifting loads and harsh roads. First, a nonlinear dynamic vehicle model is established, treating multi-source complex disturbances as a single lumped disturbance and accounting for suspension stiffness and damping nonlinearities. To stabilize the body attitude, a tri-axis controller governing the vertical, pitch, and roll motions is developed, incorporating the practical physical constraints of actuators. By employing a composite learning Radial Basis Function neural network, the controller achieves smooth approximation and precise compensation of lumped disturbances, significantly enhancing the system’s active disturbance rejection performance under complex excitations. Furthermore, the finite-time stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Finally, the strategy is evaluated under a 40% load mass mismatch and continuous random road excitations. Results indicate that the proposed strategy effectively curbs the deterioration of suspension nonlinearities during overloads, ensuring smoother dynamic transitions across all three axes. Compared to conventional backstepping control, the proposed approach reduces the root mean square values of vertical, pitch, and roll accelerations by 19%, 13%, and 35%, respectively. Ultimately, this framework effectively improves vehicle stability and disturbance rejection, providing a robust reference for heavy-duty vehicle chassis control. Full article

To address the challenges of unbalanced task allocation, high inter-UAV collision risks, and lagging interception guidance in multi-UAV cooperative missions within complex urban low-altitude environments, a cooperative interception strategy integrating load-balanced allocation, k-nearest neighbor (k-NN) cooperative obstacle avoidance, and adaptive predictive guidance is proposed. First, a load-balanced Hungarian algorithm is developed at the task allocation layer. The integration of a multi-dimensional distance-angle threat assessment model and a nonlinear load penalty mechanism resolves the issues of resource idling and target overloading inherent in traditional one-to-one allocation, thereby achieving optimal resource configuration for saturated cooperative interception. Second, at the path planning layer, a cooperative obstacle avoidance algorithm based on k-NN nonlinear repulsion is introduced. By exclusively considering the dynamic repulsive fields of local nearest neighbors alongside scale-adaptive parameter regulation, this approach maintains safe formation spacing while reducing the computational complexity from $O\left(n^2\right)$ to $O\left(k\right)(k\ll n)$, significantly enhancing flight robustness in dense airspaces. Finally, at the terminal guidance layer, an adaptive look-ahead guidance model incorporating motion prediction is constructed to mitigate the overshoot and lag defects associated with classical pure pursuit algorithms during the interception of highly maneuverable targets. The implementation of linear extrapolation and dynamic gain regulation facilitates a paradigm shift from “passive pursuit” to “active interception.” Simulation results demonstrate that the proposed algorithm yields substantial improvements in task allocation efficiency, collision risk mitigation, and overall success rates across red-blue UAV swarm confrontation scenarios of varying scales. These findings provide a viable cooperative defense framework against large-scale, highly maneuverable unmanned aerial vehicle (UAV) swarm intrusions. Full article

This study examines how decision uncertainty shapes consumers’ Fear of Better Options (FOBO), and subsequently is associated with ditto consumption, while assessing FOBO’s mediating role and the moderating effects of emotional state (Fear of Missing Out, FOMO/Joy of Missing Out, JOMO) and cultural differences (China/Korea). Using survey data from 682 new energy vehicle consumers in China and Korea, structural equation modeling was applied to test the proposed framework. The results reveal that choice overload and price fluctuation significantly increase both FOBO and ditto consumption, while obsolescence risk does not show a significant direct effect. Notably, time pressure negatively influences FOBO but positively affects ditto consumption, suggesting a dual-path mechanism in decision-making under time constraints. FOBO partially mediates the effects of choice overload and price fluctuation on ditto consumption. Moreover, emotional state and cultural differences moderate these relationships: FOMO amplifies, whereas JOMO mitigates the transmission effect of FOBO. Chinese consumers display stronger overall effects compared with their Korean counterparts. This study expands upon uncertainty avoidance theory by incorporating FOBO into consumer decision-making models, providing insights into how decision uncertainty, along with cultural and emotional factors, can inform marketing strategies. Full article

The rapid growth of electric mobility is reshaping active distribution networks (ADNs), where electric vehicle charging stations (EVCS) introduce spatially concentrated, time-dependent, and highly simultaneous demand. This paper develops a network-oriented framework to evaluate EVCS integration in ADNs by coupling Colombian EV demand characterization, photovoltaic (PV) generation, battery energy storage system (BESS) operation, and AC power flow feasibility. The framework is applied to a 33-bus distribution feeder through four EVCS deployment cases and three support architectures: PV-only, PV–BESS colocated, and PV–BESS dispersed operation. The results show that non-coordinated EVCS deployment may increase losses, reduce voltage margins, and produce thermal overloads when feeder electrical sensitivity is ignored. They also reveal that optimized EVCS siting is insufficient under PV-only support, since PV generation lacks the controllability required to reshape feeder power flows during charging peaks. By contrast, BESS-assisted architectures substantially improve feeder operation, with dispersed storage achieving the best performance by decoupling charging demand locations from grid support locations. SOC and SOH analyses further demonstrate that storage feasibility and degradation must be assessed together with voltage, loading, and loss indicators. The proposed framework provides an operationally consistent basis for technically feasible EVCS planning in ADNs, linking local EV demand characterization, AC feasibility, support-architecture selection, and battery lifetime assessment. Full article

The electrification of field service fleets introduces complex constraints: shift limits, overtime fairness, and battery–range feasibility. This paper proposes the Multi-Shift Single Electric Vehicle Routing Problem under Possibilistic Uncertainty (MS-SEVRP-PU), a formulation focused on a single-vehicle multi-shift planning unit and capturing imprecise travel/service times and state-of-charge dynamics. Travel durations and energy consumption are modelled as triangular fuzzy numbers to reflect expert knowledge when probabilistic data is limited. A closed-form credibility function evaluates overtime risk, while an Ordered Weighted Averaging (OWA) aggregation of per-shift risks ensures fairness by discouraging systematic overload on specific shifts. To solve this multi-objective problem, we develop a Pareto-Conditioned Transformer with risk-aware and battery-conscious large neighbourhood search (PCT-RABLNS), combining a preference-conditioned attention policy with targeted local search. Computational experiments on calibrated municipal maintenance case studies indicate that PCT-RABLNS improves hypervolume by 2–5% over strong baselines and reduces maximum shift overtime risk by 15–25%, with a marginal makespan overhead of only 1–3%. The results demonstrate that the proposed framework is a promising decision-support approach for energy-aware, risk-fair, and operationally compliant planning of single-vehicle, multi-shift electric service operations, jointly integrating multi-shift routing, fuzzy uncertainty, and preference-conditioned reinforcement learning. The paper also discusses how the framework can be extended to multi-vehicle settings. Full article

With the large-scale integration of new energy, electric vehicles, and other new loads, disorderly electricity consumption has led to surging peak loads and heightened overload risks for distribution transformers. Particularly in aging, high-density urban areas constrained by the cost and space limitations of upgrading distribution facilities, there is an urgent need to tap into the flexible load control potential of existing power distribution systems to ensure system safety. This paper proposes a multi-timescale cooperative scheduling framework for flexible loads in distribution systems, deeply integrating the dynamic load capacity of transformers with the dispatchable characteristics of a flexible load. First, a day-ahead scheduling layer based on multi-agent reinforcement learning is constructed to optimize electricity plans and smooth peak–valley loads in the distribution system. Second, a dynamic transformer-rating model for distribution transformers is established to uncover their dynamic load capabilities under varying environmental conditions. Finally, an intraday scheduling layer for flexible loads is developed. It dynamically matches the regulation demands of distribution transformers and flexible loads via real-time optimization of consumption strategies to address electricity price fluctuations and user behavior randomness. Case study results demonstrate that the methods described in this paper effectively reduce power load fluctuations, ensuring the safe and stable operation of distribution and power supply systems. Full article

In autonomous vehicle trajectory tracking, lane-keeping control often relies on high-order robust controllers designed with multiple performance requirements encoded via weighting functions. Such designs typically entail a significant computational cost that can overload processors already devoted to demanding tasks, such as computer vision. This work introduces an interlacing strategy for the implementation of a given robust state-space controller, with the aim of reducing its computational burden while preserving acceptable closed-loop behavior. The approach operates directly on a state-space realization and is extendable to high-order MIMO controllers, considering both diagonal (modal) and balanced realizations combined with different input–output update schemes. The method is illustrated through simulations under conditions representative of an automotive test-track circuit, indicating that substantial computational savings can be achieved at the expense of a moderate deterioration of the closed-loop response. Full article

To address the challenges of accelerated deterioration of concrete bridges caused by overloaded vehicles, this paper proposes a multi-stage heterogeneous visual framework for overloaded vehicle identification. First, a block-wise foreground–background separation method based on two-dimensional correlation coefficients is introduced and integrated with an improved Gaussian Mixture Model (GMM) to achieve dynamic background modeling and robust foreground extraction from images. Next, the Fuzzy C-Means (FCM) clustering algorithm is employed to automatically localize vehicle regions. Subsequently, Histogram of Oriented Gradients (HOG) features of vehicle candidate regions, reduced by Principal Component Analysis (PCA), are extracted and combined with a Support Vector Machine (SVM) to eliminate non-vehicle objects. Finally, an enhanced YOLOv8 model is constructed for axle-count-based overloaded vehicle detection, in which Inception modules are embedded into the CSP Darknet backbone to capture multi-scale deep hierarchical features. Meanwhile, Canny edge detection and affine transformation are fused to optimize axle-counting recognition, and overloaded vehicles are classified in accordance with the Chinese national standard GB1589-2016. Experimental results on real-world concrete bridge surveillance scenarios show that the proposed method can significantly suppress noise in vehicle foreground extraction. After SVM post-processing, the vehicle purification accuracy reaches 98.75%, with a precision of 100% for the non-vehicle category. Compared with the vanilla YOLOv8, the proposed multi-stage heterogeneous visual framework improves the precision, recall, and mAP@50 by 8%, 12.5%, and 7.2%, respectively, for heavy-duty vehicle axle recognition. The axle-feature-based heavy vehicle recognition method achieves an overall identification accuracy of 92%. Full article

Electric vehicles (EVs) are essential for sustainable urban mobility, coordinating transportation demands with energy distribution networks. However, uncoordinated EV charging neglects trip chain continuity, inducing spatial–temporal congestion and overloading local charging capacities. Thus, effectively guiding EVs is a key problem in mitigating traffic emissions and preventing power grid-side stress. In this paper, a two-stage dynamic routing framework within a traffic–energy coordination architecture is proposed, integrating an AHP–Entropy–TOPSIS model for station selection and an Improved Ant Colony Optimization algorithm for trajectory execution. Using this framework, a series of macro–micro simulations on the Sioux Falls network was conducted alongside a congestion-driven dynamic pricing mechanism. The results indicate that the pricing strategy facilitates spatial load balancing through peak shaving at core nodes. Compared to conventional standard meta-heuristic baselines, this framework reduces average economic costs by 28.9% while ensuring battery safety and limiting indirect carbon emissions. The proposed framework provides a multi-objective navigation solution that prevents cross-layer decision fragmentation, supporting the sustainable development of smart city infrastructure. Full article

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article