Search Results (1,574)

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles. Full article

The present work focuses on the inverse identification of loads acting on wing-like geometries, through strain measurements. These loads are considered quasi-static and considered acting at discrete stations across the span of the wing. A demonstrative case study is investigated, focusing on a complex composite structure, an Unmanned Aerial Vehicle (UAV) fin. To achieve this, a high-fidelity Finite Element model is developed, with “virtual” strain data generated through simulations. The technical challenge of optimal sensor placement is addressed with D-optimal designs, which promise sensor networks (sensor locations and strain components) that produce minimal uncertainty propagation from strain measurements to load estimates. These designs are obtained through the implementation of Genetic Algorithms. Different sensor networks with an increasing number of sensors are evaluated in order to identify a further reduction in epistemic uncertainty posed by the problem’s ill-conditioned nature. Full article

This study proposes a dynamic incentive-based vehicle-to-grid (V2G) strategy grounded in the battery state of health (SOH) to enhance the incentive for electric vehicles (EVs) to participate in grid peak shaving and to mitigate the load fluctuations and grid stability issues caused by large-scale EV grid integration. This strategy constructs a mobility-chain-based charging demand model and establishes a quantitative relationship between the depth of discharge (DOD) and battery lifespan degradation. It incorporates a segmented dynamic incentive mechanism that integrates load fluctuation compensation with SOH degradation compensation. This study employed multi-objective optimization to minimize both grid load fluctuations and user charging costs. Results demonstrate that this strategy effectively achieves optimized regulation of the grid load curve while maximizing economic benefits for EV users. Full article

In cold regions, flexible pavements are vulnerable to frost-induced damage, necessitating effective insulation strategies. Foam glass aggregate (FGA) insulation layers, made from recycled glass, offer promising thermal insulation properties but are mechanically fragile and susceptible to permanent deformation under repeated loading. Manufacturers provide technical recommendations, particularly regarding load limits for installation and the dimensions of the thermal protection layer. These are considered insufficient to assist pavement designers in their work. The definition of critical criteria for permissible loads was deemed necessary to design mechanically durable structures using this alternative technology. This study investigates the critical stress conditions that FGA layers can tolerate within flexible pavement systems to ensure long-term structural integrity. Laboratory cyclic triaxial tests and full-scale accelerated pavement testing using a heavy vehicle simulator were conducted to evaluate the resilient modulus and permanent deformation behavior of FGA. The results show that FGA exhibits stress-dependent elastoplastic behavior, with resilient modulus values ranging from 70 to 200 MPa. Most samples exhibited plastic creep or incremental collapse behavior, underscoring the importance of careful stress management. A strain-hardening model was calibrated using both laboratory and full-scale data, incorporating a reliability level of 95%. This study identifies critical deviatoric stress thresholds (15–25 kPa) to maintain stable deformation behavior (Range A) under realistic confining pressures. FGA performs well as a lightweight, insulating, and draining layer, but design criteria remain to be defined for the design of multi-layer road structures adapted to local materials and traffic conditions. Establishing allowable critical stress levels would help designers mechanically validate the geometry, particularly the adequacy of the overlying layers. These findings support the development of mechanistic design criteria for FGA insulation layers, ensuring their durability and optimal performance in cold climate pavements. Full article

This paper presents a high-efficiency bidirectional multidevice interleaved SEPIC–ZETA DC–DC converter for electric mobility applications. The proposed converter offers key advantages, including reduced current and voltage ripple at both the input and output ports, achieved through a port ripple frequency six times higher than the switching frequency. Additionally, the required magnetic and capacitor volume is significantly reduced due to an inductor ripple frequency twice the switching frequency, leading to minimized power losses, reduced stress on power components, and enhanced efficiency. The use of a multidevice structure facilitates more efficient inductor volume optimization and provides improved fault redundancy. The converter is particularly suited for electric vehicle energy management systems, enabling efficient energy management among the various subsystems. It operates in open-loop mode, and this manuscript details the steady-state operating principle under continuous conduction mode. Design guidelines for parameter selection, comprehensive mathematical derivations, and a comparative analysis with existing DC-DC converters are presented. To validate the proposed topology, a 5 kW laboratory prototype was developed and tested across a wide range of load conditions. The experimental results confirm the converter’s high performance, achieving a peak efficiency of 98.6% at rated power. Full article

Multi-agent path planning for Unmanned Aerial Vehicles (UAVs) in agricultural data collection tasks presents a significant challenge, requiring sophisticated coordination to ensure efficiency and avoid conflicts. Existing multi-agent reinforcement learning (MARL) algorithms often struggle with high-dimensional state spaces, continuous action domains, and complex inter-agent dependencies. To address these issues, we propose a novel algorithm, Multi-Agent Transformer-based Soft Actor–Critic (MATRS). Operating on the Centralized Training with Decentralized Execution (CTDE) paradigm, MATRS enables safe and efficient collaborative data collection and trajectory optimization. By integrating a Transformer encoder into its centralized critic network, our approach leverages the self-attention mechanism to explicitly model the intricate relationships between agents, thereby enabling a more accurate evaluation of the joint action–value function. Through comprehensive simulation experiments, we evaluated the performance of MATRS against established baseline algorithms (MADDPG, MATD3, and MASAC) in scenarios with varying data loads and problem scales. The results demonstrate that MATRS consistently achieves faster convergence and shorter task completion times. Furthermore, in scalability experiments, MATRS learned an efficient “task-space partitioning” strategy, where the UAV swarm autonomously divides the operational area for conflict-free coverage. These findings indicate that combining attention-based architectures with Soft Actor–Critic learning offers a potent and scalable solution for high-performance multi-UAV coordination in IoT data collection tasks. Full article

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading. Full article

One of the bottlenecks hindering the applications (e.g., vehicle navigation) of blockchain–UCAN is privacy. A sharded blockchain can protect vehicle data to a certain extent. However, unbalanced shard loads lead to low throughput and poor feature extraction in blockchain–UCAN. This paper proposes an optimal image binarization method (OTSU-GK) to enhance image features and reduce the amount of uploaded data, thereby improving throughput. Specifically, OTSU-GK uses a Gaussian kernel method where the parameters are optimized using grid search to improve the calculation of the threshold. Additionally, we have designed a Node Load Score (NLS)-based sharding blockchain, which considers the number of historical transactions, transaction types, transaction frequency, and other metrics to balance the sharding loads and further improve throughput. The experimental results show that OTSU-GK improves by 74.3%, 58.7%, and 83% in SSIM, RMSE/MAE/AER, and throughput. In addition, it reduces IL by 70.3% compared to other methods. Full article

In the context of the rapid development of the new energy vehicle industry, how to achieve the mixed production of fuel vehicles and electric vehicles has become an important issue for the transformation and flexible manufacturing of automotive production lines. This paper addresses the balance problem of the mixed assembly line for electric vehicles and fuel vehicles and proposes a mathematical modeling method based on the product structure differences and workstation sharing. An improved genetic algorithm is designed for optimization. The established optimization model includes mathematical models of process priority relationships, cycle time constraints, synchronization constraints, and exclusive process co-placement constraints, with the optimization goals of minimizing workstation quantity and balancing workstation load. To solve such models, the decoding process of the genetic algorithm is redesigned in the algorithm design. The improved genetic algorithm can be well used to solve the workstation-sharing model. A case study of the chassis assembly line of an automotive manufacturing enterprise is used for verification. The results show that the method considering workstation sharing can effectively reduce the number of workstations, improve the distribution of workstation loads, and increase the utilization rate of the production line, while ensuring the cycle time constraints. The conclusions of this study expand the theoretical framework of the balance problem of mixed assembly lines and provide practical references for the transformation of fuel vehicle production lines into new energy vehicles. Full article

This paper proposes a comprehensive framework, Theory–Simulation–Experimental Verification, for the elasto-aerodynamic analysis of elastic foil gas bearings (EFGBs). In contrast to many studies that approximate the foil structure using simplified two-dimensional models, the present work adopts a macro-element beam theory model that incorporates the actual 3D geometry, nonlinear elasticity, and frictional contact effects, and couples it directly with the Reynolds equation. To improve accuracy and robustness, the macro-beam results are validated against a fully coupled fluid–structure interaction (FSI) model developed in COMSOL Multiphysics. Emphasis is placed on quantifying the influence of foil thickness, clearance, and eccentricity, where the pressure distribution, foil deflection, and load capacity are obtained through the coupled solver. The results reveal that increasing foil thickness from 0.1 mm to 0.2 mm elevates the peak gas film pressure from 1.36 × 10⁵ Pa to 1.97 × 10⁵ Pa while simultaneously reducing displacement and pressure fluctuations, thereby enhancing bearing stability. Smaller clearances are shown to increase load capacity but also induce stronger oscillatory flow behavior, indicating a stiffness–stability trade-off. Additionally, prototype experiments with a 0.05 mm clearance confirm practical lift-off at 4300–7000 rpm under 10–30 N external loads, with measured torques of 0.18–0.30 N·m. By combining computational efficiency, 3D fidelity, and experimental validation, the proposed framework provides quantitative guidance for the design and optimization of EFGBs used in high-speed turbomachinery, such as aviation and compact energy systems, including turbine-based air-cycle refrigeration units and small gas-turbine rotors for unmanned aerial vehicles. Full article

The controller of the energy management system must be capable of meeting the rapid and dynamic demands of fuel cell electric vehicles (FCEVs) without compromising its performance and durability. The performance of FCEVs can be enhanced through powertrain hybridization with battery and ultracapacitor systems. The overall dynamic optimization of the energy between the batteries/ultracapacitors and the Proton Exchange Membrane Fuel Cell (PEMFC) output can play an important role in hydrogen fuel economy and the durability of vehicle systems. The present study investigates the system’s efficiency and fuel consumption in European Drive Cycles when employing diverse energy management strategies. This investigation utilizes a novel switch real-time strategy (SWA_RTO), which is founded on an A-factor algorithm that alternates between the most effective Real Time Optimization (RTO) strategies. The objective of this paper is to underscore the significance of algorithmic optimization by presenting the optimal results obtained for the fuel economy of the SWA_RTO strategy. These results are compared with the basic RTO strategy and the static Feed-Forward (sFF) reference strategy. The load demand during driving cycles is primarily determined by the PEMFC system. Minor discrepancies in power balance are addressed by the hybrid battery and ultracapacitor system. Consequently, the lifespan of the subject will increase, and the state of charge (SOC) will no longer be a factor in monitoring. Full article

Underground mining environments present elevated occupational health risks, primarily due to substantial exposure to diesel exhaust emissions within confined and poorly ventilated spaces. This study assesses the real-world performance of two advanced retrofit emission control systems—Proventia NOxBuster and Purifilter—installed on underground mining trucks operating in a Spanish mine. Emissions of carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO₂) were quantified using a Testo 350 multigas analyser, while ultrafine particle (UFP) concentrations were measured with an Engine Exhaust Particle Sizer (EEPS-3090) equipped with a thermodiluter. Controlled tests under both idling and acceleration conditions revealed substantial reductions in pollutant emissions: CO decreased by 60–98%, NO by 51–92%, and NO₂ by 20–87%, depending on the system and operational phase. UFP concentrations during idling dropped by approximately 90%, from 542,000 particles/cm³ in untreated trucks to below 50,000 particles/cm³ in retrofitted vehicles. Under acceleration, the Proventia NOxBuster achieved reductions exceeding 95%. Conversely, Purifilter-equipped trucks exhibited a counterintuitive increase in UFPs within the 5.6–40 nm range, potentially due to ammonia slip events during selective catalytic reduction (SCR). Despite these discrepancies, both systems demonstrated considerable mitigation potential, albeit highly dependent on exhaust temperature (optimal: 200–450 °C), urea dosing precision, and maintenance protocols. This work underscores the necessity of in situ performance verification, regulatory vigilance, and targeted intervention strategies to protect underground workers effectively. Further investigation is warranted into the long-term health benefits, system durability, and nanoparticle emission dynamics under variable load conditions. Full article

Autonomous vehicles (AVs) promise improved safety and sustainability, yet their sophisticated sensing, computing, and communication systems impose auxiliary power loads of 1.5–3.2 kW, risking an increase of up to 45% in global transport energy demand by 2040 if left unaddressed. Existing energy management strategies fail to jointly optimize propulsion and autonomy subsystems under real-world dynamic traffic, treat ADAS loads as static, and lack statistically rigorous validation. This paper proposes a novel Adaptive Neuro-Fuzzy Inference System (ANFIS)-PID framework that integrates (i) 5 s V2X traffic preview, (ii) online PID gain scheduling, and (iii) energy-aware rule pruning for real-time energy allocation. Validated on a real-world trajectory dataset, the approach consistently reduces fuel consumption by up to 4.4% over pure fuzzy logic, 0.05% over FL-RWOA, 1.16% over FL-GWO, and 2.39% over FL-PSO across 25–100 km segments (paired t-test, p ≤ 0.001 on 50 random segments). Additional benefits include 18% faster transient response and 18% lower inference computational load compared to metaheuristic baselines. Scaled to fleet level, the 0.51 L/100 km average saving equates to over CAD 100 million annual savings in Canada. The hybrid neuro-fuzzy architecture offers a deployable, adaptive solution for sustainable autonomous transportation. Full article

Background: Kazakhstan’s transport system plays a key role in Eurasian logistics due to its position along the Middle Corridor. However, multimodal freight transport remains under-optimized due to infrastructure bottlenecks, uneven cargo flows, and limited digital tools for forecasting and planning. Methods: This study presents the development of an agent-based simulation model for analyzing multimodal transportation in Kazakhstan. The model integrates railway, road, and maritime components, simulating cargo flows across export, import, and transit scenarios. Key agents include orders, transport vehicles, logistics hubs, and border checkpoints. The model is implemented in AnyLogic 8.9 and calibrated using a mix of official statistics, industry data, and field estimates. Results: The simulation replicates key logistics processes, identifies congestion points, and evaluates delivery performance under different scenarios. Experiments demonstrate how bottlenecks at terminals and border crossings affect delivery times, vehicle utilization, and hub load. The model allows testing infrastructure development options and scheduling policies. Conclusions: The approach enables a dynamic assessment of logistics efficiency under uncertainty and can support decision-making in transport planning. The novelty lies in the integrated simulation of multimodal freight flows with infrastructure constraints. The model serves as a foundation for digital twin applications and scenario-based planning. Full article