Search Results (3,554)

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a /Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

Under the trend of AI-defined vehicles, multi-agent collaboration has become the core feature for intelligent vehicles to deliver superior user experience (UX). Traditional linear and independent evaluation methods can no longer adapt to the new technical characteristics and logic. Taking the agents of four functional domains—intelligent driving, intelligent cockpit, intelligent vehicle control, and intelligent connectivity—and their cross-domain collaborative relationships as research objects, this study constructs a UX evaluation index system consisting of five primary indicators and 14 secondary indicators. Innovatively, the analytic network process is adopted for indicator weight allocation, which effectively characterizes the interdependencies among indicators caused by multi-agent collaboration. Meanwhile, the coupling coordination theory is introduced to construct a comprehensive UX index, enabling quantitative evaluation of the balanced development level across the five dimensions. The results show that in intelligent vehicle UX, excellence in a single dimension does not equal excellent overall UX. Only through the collaborative upgrading of multiple agents and balanced development of the five dimensions can the comprehensive UX be maximized. This study further reveals the UX mechanism of multi-agent collaboration in intelligent vehicles and determines the optimal collaborative evolution path based on the dynamic programming algorithm, providing theoretical support and practical guidance for automakers in rational product development planning. Full article

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size $48\times 96\times 3$ were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems. Full article

COVID-19 was a prolonged public-health shock that disrupted mobility, access to services, and household spending. Although the official U.S. poverty rate declined to 11.1%, the Supplemental Poverty Measure rose to 12.9%, suggesting that material hardship persisted unevenly across places. This study asks whether pre-existing livelihood vulnerability and local epidemic burden translated into geographically concentrated consumption losses during 2020–2022. Because sustained consumption loss can erode households’ health-related spending, tracking where spending declines concentrate helps connect local social and environmental conditions to how communities withstand a health crisis. We analyze consumer expenditure, unlike prior research relying on aggregate retail sales, to capture fine-grained economic strains as a proxy for shock-absorption capacity. A Livelihood Vulnerability Index (LVI) was calculated for each U.S. county using 16 socio-economic variables, and counties were classified as high- or low-risk. A multilevel model then examined how socio-economic and COVID-19 factors at county and census tract levels shaped consumption changes. Higher-risk communities experienced greater consumption reductions. At the census tract level, the non-White ratio, vacancy rate, built year, per capita income, education level, and housing value were significant. At the county level, COVID-19 cases and deaths, crowding, public transportation use, and vehicle availability mattered most. These findings support place-targeted strategies that combine public-health response with socio-environmental interventions to reduce disparities rooted in pre-existing vulnerability. Full article

The planning of trajectories for Connected Autonomous Vehicles (CAVs) represents a pivotal aspect of autonomous driving technologies, enabling secure navigation within traffic environments. Traditional models for trajectory control primarily focus on the efficiency and safety of individual vehicles but often overlook the dynamics involved in vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study introduces a novel concept, the “driving risk field,” which imposes constraints on vehicular movement within designated road spaces to enhance safety. A vehicle dynamics model is developed, employing a non-linear fifth-degree polynomial to approximate the trajectory curves, with optimization performed using the Sequential Quadratic Programming (SQP) method. The efficacy of the optimized model is validated through simulations on the Prescan/Simulink plat Full article

The sixth-generation (6G) mobile networks are envisioned to deliver seamless three-dimensional(3D) coverage from ground to sky and vice versa. In parallel, aerial corridors are emerging to elevate ground-based transportation into the air, enabling smart air mobility for unmanned aerial vehicles (UAVs). The convergence of this intelligent transportation system (ITS) with 6G introduces new challenges: how to ensure reliable, efficient connectivity within aerial corridors, and how these corridors can serve as foundational sky infrastructure to advance the 6G ecosystem. This paper presents a comprehensive survey that systematically presents aerial corridors as integrated 6G sky infrastructure, unifying corridor geometry, network architecture, channel modeling, and key enabling technologies within a single framework. It conceptualizes the aerial corridor as a tube-shaped, multi-lane, bidirectional structure to manage drone-based roles, including user equipment (UE), base stations (BS), and communication relays. To support this vision, key enablers such as air-to-ground channel modeling and integrated sensing and communication (ISAC) are investigated. The proposed infrastructure aligns with the IMT-2030 vision, supporting machine-type communication, ubiquitous connectivity, and immersive services in regulated aerial space. Full article

Highway vehicle–road collaborative systems rely on the effective deployment of roadside equipment (RSE) to support both traffic sensing and communication. In mixed connected and automated vehicle (CAV) and human-driven vehicle (HDV) traffic environments, existing studies on hybrid RSE deployment have mainly focused on unilateral deployment or scenarios with a high CAV penetration rate, whereas bilateral deployment under a low-to-medium CAV penetration rate has received limited attention. To address this gap, this study proposes a bilateral hybrid deployment framework for highways, in which sensing and communication RSE (scRSE) and communication RSE (cRSE) are jointly allocated based on data sensing accuracy and communication connection probability. The proposed method is validated through a case study on the Qinglan Expressway in Shandong Province, China. The results show that the bilateral hybrid deployment method outperforms the benchmark deployment methods in both sensing and communication performance. In a representative scenario, the mean symmetric mean absolute percentage error (SMAPE) decreases from 2.36% under bilateral uniform deployment to 0.94% under bilateral hybrid deployment, while the mean communication connection probability (MCCP) increases from 82.20% to 86.29%. Moreover, the proposed method performs better than unilateral deployment strategies under the same deployment conditions. These findings indicate that the proposed bilateral hybrid deployment framework offers a practical and cost-effective solution for highway RSE allocation in mixed traffic environments, particularly under low-CAV-penetration conditions. Full article

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control. Full article

The rapid expansion of the Internet of Vehicles (IoV) necessitates high-capacity backhaul connectivity, yet the deployment of such networks under strict hardware and power constraints poses significant computational challenges for network optimization. To address this challenge, this paper investigates a joint transmit–receive beamforming optimization problem for narrowband wireless backhaul in IoV networks under constant-modulus constraints. Unlike ideal digital architectures, we focus on cost-effective analog phase shifters, which introduce strictly non-convex constant-modulus constraints, rendering the optimization problem mathematically intractable for standard solvers. Since the resulting problem is highly non-convex, we develop two structured numerical methods: an iterative alternating optimization (AO) method and a joint optimization (JO) method, where AO employs auxiliary WMMSE-guided alternating updates together with constant-modulus projection, while JO jointly updates both beamformers over the constant-modulus feasible set. We compare their achievable sum-rate performance with that of a CDO-based benchmark and analyze their dominant computational costs through representative Big-O complexity expressions. Furthermore, we examine the effect of SVD-based and random feasible initializations on empirical convergence behavior, runtime, and final achievable performance. Simulation results demonstrate that the proposed computational methods significantly improve achievable sum-rate performance compared with the CDO benchmark. Moreover, SVD-based initialization provides a more structured starting point and generally leads to better convergence behavior and lower runtime than random feasible initialization. The empirical timing results further show that AO exhibits faster empirical convergence and requires lower runtime, whereas JO achieves better final sum-rate performance after more iterations. Full article

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy ensures that modules consistently operate in high-efficiency regions, thereby achieving high energy conversion efficiency across a wide voltage range. First, the operational characteristics of the three-phase PWM rectifier and the dual active bridge (DAB) converters are analyzed, and their corresponding mathematical and loss models are established. Subsequently, the charging demands acquired by the charging stack are analyzed, and an adaptive variable structure control strategy is designed based on the module margin of the charging stack. When modules are surplus, the feasible range of series/parallel configurations for each port is constrained, and module combinations are optimized with the objective of minimizing system losses. When modules are insufficient, an adaptive module reservation scheduling strategy is employed to ensure temporal fairness in vehicle connection order while supplying power to multiple vehicles, effectively reducing the average charging time. Finally, the effectiveness of the proposed control strategy is validated through simulations conducted on the Matlab/Simulink platform. Results demonstrate that compared to traditional fixed-structure systems, the proposed strategy improves peak efficiency by up to 2.53% at 400 V and 1.12% at 800 V, while reducing the average charging time by 3.07% in the disconnection scenario and 12.1% in the asynchronous access scenario. Full article