Search Results (615)

This paper presents a battery cell joining solution leveraging adhesively bonded lithium-ion cells as a foundation for scalable, serviceable, and recyclable energy storage platforms. The proposed design methodology enables mechanically and electrically functional connections and supports a design concept intended for compatibility with automated manufacturing and future robotic disassembly. A123 26650-format cells were tested using Epo-Tek 430 conductive adhesive, with performance evaluated through ESR and G-force measurement experiments. The results indicate that no measurable change in electrical performance was observed within the resolution of the measurement system, while supporting a design concept intended to improve modularity and serviceability. The proposed system shows potential for further investigation in electric vehicle and industrial energy system applications, although further validation under realistic operating conditions is required. Full article

With the rapid development of intelligent manufacturing and smart logistics, object detection has become increasingly important in automated transportation systems, including automated guided vehicles (AGVs), warehouses, production workshops, and distribution operations. However, under adverse weather conditions, existing object detection methods often suffer from degraded performance because object features become blurred or less distinguishable, resulting in reduced detection accuracy. To address this issue, this study proposes an improved object detection algorithm for adverse weather conditions based on YOLOv8n. Specifically, the SimAM attention mechanism is introduced into the backbone network to enhance feature representation. An LCAHead detection head is designed to improve cross-layer feature fusion. In addition, the Wise-IoUv1 loss function is used to replace CIoU, contributing to more stable training and improved convergence. Finally, channel-wise distillation is applied to further enhance detection accuracy without increasing inference cost. Experimental results on the test set show that the proposed method achieves an mAP@0.5 of 50.8%, representing a 7.6% improvement over YOLOv8n, while maintaining an inference speed of 128 FPS. Full article

This article discusses a novel aircraft coordination algorithm for automated vertiport operation. New applications of Innovative Air Mobility (IAM) including inspection, logistics and security UAVs, Urban Air Mobility (UAM) or Regional Air Mobility (RAM) present a coordination challenge, especially near vertiports, as large numbers of vehicles with different characteristics share the airspace, and so avoiding collisions, optimizing resource usage and operating with low human intervention is important.In this paper, this problem is addressed by proposing a new formulation of the aircraft coordination problem that makes use of a discretized airspace to detect potential conflicts and collisions between cooperative and non-cooperative aircraft in the surroundings of a vertiport. The proposed algorithm not only considers the cells traversed by the aircraft, but also the set of adjacent cells, making the algorithm more conservative and robust than other algorithms found in the literature, and achieving a 100% conflict-detection rate. A mathematical model of aircraft dynamics is employed to turn high-level flight plans into detailed aircraft trajectories, using those trajectories to detect potential collisions. The deconfliction problem is formulated as a mixed-integer optimization program that computes orders of pass for every conflict while minimizing the divergence between requested time of arrival (RTA) and estimated time of arrival (ETA). This problem is implemented in OR-Tools to be solved by means of the CP-SAT solver. The validity of the solution is tested by extensive simulation, showing tactical coordination of up to 25 aircraft landing on a vertiport. Full article

On-site hydrogen refueling stations (HRS) face significant operational challenges due to the stochastic nature of hydrogen demand, creating a severe supply–demand mismatch. Under traditional pressure-based hysteresis control, this volatility forces Proton Exchange Membrane (PEM) electrolyzers into frequent start–stop cycles, accelerating degradation and reducing efficiency. In response, this study introduces an automated control framework integrating macroscopic gas-state modeling with deep-learning-based demand prediction. First, a real-gas thermodynamic model was established. Monte Carlo simulations of 100 random filling scenarios identified a robust design benchmark of 4.5 kg per vehicle. A low filling stability coefficient (5.02%) confirmed that individual thermodynamic fluctuations are negligible, validating a traffic-flow-driven demand approach. Next, a deep Long Short-Term Memory (LSTM) network was developed to forecast short-term demand. Trained on an 8784 h dataset exhibiting “double-peak” traffic patterns, the model achieved high precision on the unseen test set, yielding a Root Mean Square Error (RMSE) of 6.75 kg and a normalized RMSE (nRMSE) of 0.0987, explaining 82% of the demand variance. Finally, an LSTM-informed demand-following control strategy was formulated to enable proactive, thermally bounded operation alongside a novel “Hot Standby” mechanism. Maintaining a minimal 3.0 kg/h holding current during idle periods sustains stack temperatures above 60 °C, effectively mitigating thermal stress. Comparative simulations over 1464 h demonstrated that the proposed framework reduces detrimental cold start–stop cycles by 98.4% (from 61 to 1) and suppresses power output fluctuations by 40.7% compared to the traditional baseline. These results confirm that data-driven control significantly enhances operational stability, facilitates grid integration, and extends core equipment service life. Full article

The integration of electric vehicles with grid vehicles promotes the creation of multi-energy microgrid models. One of the aims of these models is to decrease electricity usage through Vehicle-to-Grid planning. Effective management of microgrids necessitates sophisticated automation and control systems, which can prove challenging to establish and sustain. To tackle these challenges, the author introduces a hybrid model that merges a Linear Regression model and a Feedforward Neural Network, created using Matlab software. This combined algorithm adjusts the quantity of hidden neurons to enhance performance, guided by the evaluation criteria of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error based on batteries B0005, B0006, and B0007 from the NASA PCoE Research Center Dataset. The author forecasts the lifespan of the battery that most accurately reflects its degradation, revealing important implications for the future advancement of systems that employ Linear Regression and Feedforward Neural Networks for integrating electric vehicles into Vehicle-to-Grid systems. The comparison among the training, testing, and validation stages of the methodology serves to thoroughly demonstrate its effectiveness. Furthermore, the author indicates that the LR-FFN algorithm provides predictive tools relevant for the management of V2G-compatible EV systems and performs superiorly compared to other methods noted in the existing literature. Additionally, the author aimed to specifically identify the attributes of the LR-FNN model for prospective usages, emphasizing its efficacy in developing effective microgrid management, promoting energy efficiency, and ensuring that microgrids remain secure and resilient against failures or threats. Full article

Automated Guided Vehicles (AGVs) play a crucial role in intelligent warehousing; however, effective path planning remains challenging because of obstacles, safety constraints, and the risk of suboptimal routes. This study proposes an improved Harris Hawks Optimization algorithm for AGV path planning, introducing strategies to enhance initial solution quality, balance global and local search, and avoid local optima. The proposed algorithm generates shorter, smoother, and safer paths, as demonstrated through benchmark tests, multi-scale grid-map simulations, and real-world AGV experiments. In terms of path length and computational efficiency, the enhanced algorithm significantly outperforms the original HHO, reducing average path length by 10.81% and average travel time by 11.94%. These results demonstrate that the proposed method provides a practical and reliable solution for autonomous warehouse navigation and significantly improves AGV path-planning performance. Full article

The rise of Industry 4.0 has accelerated the adoption of intelligent automation in high-throughput manufacturing environments. Automated guided vehicles (AGVs) rely heavily on magnetic guidance tracks, which are susceptible to wear, contamination, and structural degradation. These defects frequently cause AGV misalignment, emergency stops, and production downtime. This paper presents a lightweight, embedded, vision-based framework for real-time monitoring of AGV magnetic tracks using Raspberry Pi 4 cameras and Python-based computer vision algorithms. The system integrates grayscale intensity modeling, histogram-based MeanShift tracking, contour continuity analysis, and machine learning-assisted classification to detect missing segments, wear, and foreign object interference. Experimental validation on a 30 m test track and five years of industrial data (>3000 samples) demonstrate robust tracking, reliable anomaly detection, and zero false positives under nominal conditions. The proposed hybrid deterministic, ML architecture supports predictive maintenance, reduces downtime risk, and contributes to resilient Industry 4.0 material-handling systems. Full article

In automotive crash testing, trustworthy crash-test curves are essential for reliable crashworthiness assessment, yet automated anomaly detection is difficult due to limited labeled abnormal cases, event-level data scarcity, and distribution shifts across vehicle models and sensor configurations. This paper proposes MVCA-AD (Multi-View Context Augmentation for Anomaly Detection) for single-channel crash-test curves. MVCA-AD generates multiple context-rich views using deterministic time- and frequency-domain transformations to amplify subtle anomalous patterns under limited labeled supervision. A trend-aware modulation module and cross-view attention fuse these views to improve sensitivity to critical segments such as impact spikes and gradual transitions while remaining robust to noise. Experiments on three subsets derived from physical full-scale crash tests show that MVCA-AD improves Precision, Recall, F1-score, and area under the ROC curve (AUC) over strong baselines and achieves stable performance under event-level grouped evaluation across heterogeneous head and B-pillar crash-test signals. The proposed approach supports crash-test data quality control by automatically identifying abnormal curves for downstream crashworthiness assessment workflows. Full article

In unmanned aerial vehicle (UAV)-based power line inspection, multi-scale defects and complex backgrounds challenge the balance between detection accuracy, speed, and model lightweighting, limiting automated grid inspection. This paper proposes a Multi-Scale Mamba Framework (MS-Mamba) for efficient and accurate defect perception. A drone inspection dataset containing 5137 images from 14 defect categories was constructed and divided into training and validation sets with an 8:2 split. To address the large scale variation among defects, the categories are decoupled into macroscopic, mesoscopic, and microscopic groups according to physical attributes and visual scales. As the core perception engine, a lightweight state-space mechanism is designed to balance accuracy and deployability. A spatial resolution-aware hierarchical reconstruction strategy and a dynamic feature selection mechanism are integrated to enhance feature extraction, reduce background redundancy, and improve small-target representation. Compared with the YOLOv5s baseline, MS-Mamba achieves an mAP@0.5 of 0.749, corresponding to a 15.6 percentage-point improvement, while reducing parameters by 0.13 M and computational cost by 1.7 GFLOPs. Ablation studies and visual analyses further confirm fewer missed and false detections in complex backgrounds. The developed end-to-end inspection system was validated through closed-loop engineering tests, demonstrating strong potential for industrial deployment. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

Modern applications are increasingly built around networking, collaboration, and automation. Drones, or Unmanned Aerial Vehicles (UAVs), are a key part of this shift. Many complex missions require multiple UAVs to work together as a team, which means deciding how to group them efficiently is a real optimization challenge. This paper introduces EPICEAg (Enhanced Preference-Inspired Co-Evolutionary Algorithm with goal vectors), a new algorithm for forming optimal UAV teams, called coalitions. EPICEAg builds on an existing algorithm called PICEAg but adds three important improvements: it uses k-medoids clustering through the Partitioning Around Medoids (PAM) algorithm for more reliable team leader selection, and applies two advanced sorting methods—shift-based density estimation and epsilon-ranking—to manage the complexity of the search. The algorithm optimizes seven goals at once: how well tasks are completed, how efficiently resources are used, how reliable the team and its communications are, how trustworthy the individual drones are, and how much energy they have left. Tests across several mission scenarios show that EPICEAg consistently performs better than PICEAg, NSGA-II, and MOPSO. Full article

This article draws on public opinion surveys conducted as part of the AV-PL-ROAD project, “Polish Road to Automation of Road Transport”. Although selected findings from this survey material were published in 2023, the earlier study was limited to descriptive statistical analysis. The present paper re-examines the same empirical dataset through a different analytical framework focused on latent-structure reconstruction, using a different analytical framework focused on latent-structure reconstruction, providing a more structured and informative interpretation of perceptions of autonomous vehicles in Poland. The study combines within-respondent standardization, Principal Component Analysis (PCA), and k-means clustering to identify the dominant dimensions of perception and recurring perception profiles, complemented by qualitative insights from focus group interviews (FGI) used to support interpretation. The results indicate that perceptions of autonomous vehicles are not one-dimensional, but are organized around three main axes: systemic benefits versus implementation barriers, technological trust and information security, and regulatory-ethical readiness linked to deployment conditions. The analysis also reveals four recurring perception profiles that do not map directly onto simple demographic divisions and are better understood in relation to operational and institutional context. In addition, statistically significant differences between clusters were confirmed using nonparametric tests (Kruskal–Wallis with Dunn–Šidák post hoc analysis). The main contribution of the paper is methodological: it illustrates that previously analyzed survey data can yield structurally informative insights, including the identification of latent dimensions, perception profiles, and statistically significant differences between clusters when reinterpreted through a latent-space approach rather than conventional descriptive methods. The findings provide additional evidence on the social and institutional conditions of transport automation in Poland and provide a more robust analytical basis for future mobility policy and implementation strategies. Full article

This study presents a data-driven framework for the mechanistic interpretation of asphalt pavement responses using an integrated smart sensing and monitoring system deployed on a national highway in Thailand. A fully instrumented pavement test section was developed, incorporating a multi-sensor embedded network and a field data acquisition platform integrated with weigh-in-motion (WIM) technology. The system consists of 54 sensors, including strain gauges, pressure cells, moisture sensors, and thermocouples, installed at multiple depths to capture high-resolution stress–strain responses under controlled heavy-vehicle loading. Field measurements were analyzed and compared with classical mechanistic models, including Boussinesq’s theory, Odemark’s equivalent thickness method, and Burmister’s multilayer elastic theory. The results demonstrate good agreement for vertical stress predictions in deeper layers, while significant discrepancies were observed in strain responses, particularly in the asphalt layer, where measured tensile strains were up to 2.5 times higher than theoretical estimates. The findings indicate that conventional elastic models provide useful first-order approximations; however, discrepancies were observed in representing the viscoelastic behavior of asphalt materials under real loading conditions. Furthermore, the integration of sensor data with traffic loading information confirms that axle load magnitude is the dominant factor governing pavement responses, whereas vehicle speed primarily influences load duration. The proposed framework demonstrates the potential of smart sensing systems for enabling automated, data-driven pavement analysis and supporting digital twin-based infrastructure management. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

Ground-vehicle manufacturers and their suppliers must shorten development cycles to remain competitive. This paper presents a novel design framework that accelerates the traditional V-model development lifecycle by enabling digital twins and hardware-in-the-loop testing. As a case study, the design of active suspension actuators to address comfort shortfalls that hinder automated driving has been selected. A hybrid suspension architecture combining a continuously controlled hydraulic damper with an auxiliary electromechanical actuator has been proposed. The hybrid system achieves lower energy consumption than purely electromechanical suspensions while overcoming the bandwidth limitations of conventional hydraulic active suspensions. Control is implemented using the Triple Skyhook algorithm and benchmarked against a baseline strategy. Results demonstrate that the proposed framework accelerates actuator design iteration and that the proposed suspension delivers superior performance with improved efficiency and bandwidth. Full article