Search Results (182)

Battery electric vehicles (BEVs) play a key role in reducing CO₂ emissions and enabling a climate-neutral economy. However, they suffer from reduced range in cold conditions due to electric cabin heating. Electrically heated thermal energy storage (TES) systems can decouple heat generation from demand, thereby preventing a loss of range. For this purpose, a novel concept based on a directly electrically heated ceramic solid media TES is investigated, aiming to achieve high storage density while enabling both high charging and discharging powers. To assess the feasibility of the proposed TES concept in BEVs, a holistic evaluation of central aspects is conducted, including experimental characterization for material selection, experimental investigations on electrical contacting, and simulations of the electrothermal charging and thermal discharging processes under vehicle-relevant conditions. As a result of the material characterization, a promising material—a silicon carbide-based composite—was identified, which meets the electrothermal requirements under typical household charging conditions and allows reliable operation with silver-metallized electrodes. Design studies with this material show gravimetric energy densities—including thermal insulation demand—exceeding 100 Wh/kg, storage utilization of up to 90%, and fast charging within 25 min, while offering 5 kW at flexible temperature levels for cabin heating during thermal discharging. These results show that the basic prerequisites for such storage systems are met, while further development—particularly in terms of material improvements—remains necessary. Full article

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

This paper presents an optimised signal processing framework for contactless physiological monitoring using Frequency Modulated Continuous Wave (FMCW) radar within automotive environments. This research focuses on enhancing heart rate (HR) and heart rate variability (HRV) detection from radar signals by integrating radar placement optimisation and advanced phase-based processing techniques. Optimal radar placement was evaluated through Signal-to-Clutter Ratio (SCR) analysis, conducted with multiple human participants in both laboratory and dynamic driving simulator experimental conditions, to determine the optimal in-vehicle location for signal acquisition. An effective processing pipeline was developed, incorporating background subtraction, range bin selection, bandpass filtering, and phase unwrapping. These techniques facilitated the reliable extraction of inter-beat intervals and heartbeat peaks from the phase signal without the need for contact-based sensors. The framework was evaluated using a Walabot FMCW radar module against ground truth HR signals, demonstrating consistent and repeatable results under baseline and mild motion conditions. In subsequent work, this framework was extended with deep learning methods, where radar-derived HR and HRV were benchmarked against research-grade ECG and achieved over 90% accuracy, further reinforcing the robustness and reliability of the approach. Together, these findings confirm that carefully guided radar positioning and robust signal processing can enable accurate and practical in-cabin physiological monitoring, offering a scalable solution for integration in future intelligent vehicle and driver monitoring systems. Full article

This paper aims to fill a critical cap in hybrid-electric propulsion (HEP) research by investigating the feasibility of its application on a ‘large-cabin’ business aircraft by 2040, for which key requirements are a long range of at least 6297 km (3400 nmi), and a cruise speed of Mach 0.85. Based upon a representative baseline ‘large-cabin’ aircraft, a time-stepping simulation for the distinct phases of an NBAA mission, consisting of takeoff, climb, cruise, landing, and a reserve segment is developed for turbofan, series, and parallel architectures. The simulation enables analysis of range, specific air range, battery weight, battery volume, and energy consumption for various degrees of hybridization and battery specific energy densities. The results find that while both series and parallel architectures are able to meet the requisite range targets, the parallel architecture is better suited as the overall drivetrain weight is lower. The parallel HEP architecture enables the aircraft to fly a maximum distance of 7082 km (3824 nmi), with a 5% energy hybridization. Over a typical 5556 km (3000 nmi) mission this equates to fuel savings of 847 kg compared to a turbofan. The HEP ‘large-cabin’ aircraft is viable provided battery technology reaches a specific energy density of at least 800 Wh/kg. Full article

Early-stage dynamic responses of naval structures under underwater explosion shock loads exhibit high-frequency, intense amplitude fluctuations and short durations, serving as critical factors for the development of plastic deformation and other damage characteristics. These structural dynamics demonstrate prominent nonlinear and non-stationary features. This study focuses on the nonlinear evolutionary patterns of early-stage plastic shock responses in underwater explosion-impacted ship structures. Utilizing phase space reconstruction, unimodal mapping, and symbolic dynamics theory, we analyze the nonlinear and non-stationary characteristics along with their evolutionary patterns in experimental data. First, scaled model experiments under varying shock factors were conducted based on a stiffened cylindrical shell prototype, investigating the spatiotemporal evolution of nonlinear and non-stationary dynamic responses under different shock loads while characterizing their uncertainty features. Second, model tests were performed on deck-type cabin structures and plate frameworks derived from a naval vessel’s deck prototype, further analyzing the evolutionary patterns of early-stage plastic dynamic responses and verifying the method’s effectiveness and universality. Research findings indicate that (1) early-stage plastic shock responses of ships under underwater explosions exhibit multiple dynamical behaviors including chaotic motion, periodic motion, and quasi-periodic motion, and (2) during the initial plastic phase, orbital parameters approximate 0.8, providing guidance for test condition setup and initial parameter selection in underwater explosion experiments on naval structures. Full article

In the design of complex products, change propagation often leads to overlapping impact paths, structural conflicts, and decreased coordination efficiency, especially in multi-source change scenarios. These issues limit the effectiveness of existing path extraction and management methods. This study addresses the challenge of managing multi-source change propagation by proposing a novel optimization method. The method involves constructing a multi-layer propagation network model that integrates structural, functional, and form-level information and combines propagation risk assessment with frequency feedback mechanisms. By employing a path cost function based on node risks and propagation probabilities, the model accurately evaluates the costs of change propagation and identifies high-risk paths while prioritizing those with lower costs. Additionally, a multi-source path combination optimization approach is developed, and it dynamically adjusts path costs to reduce overlap and conflicts, improving coordination between paths in multi-source change scenarios. The proposed approach was validated through a case study based on the design data of an intelligent cabin system, where the constructed propagation network reflects real-world structural, functional, and form-level dependencies. The experimental results show that the proposed method reduces total propagation cost by an average of 27.3% and lowers node-level conflict rate by 41.6% compared with baseline methods. Tests on different complex products confirmed consistent advantages over baselines with $p<{10}^{-5}$, while also reducing redundancy and conflicts. The method maintained these advantages across networks of different sizes with linear scalability. Sensitivity analysis confirms that frequency-feedback weighting $\alpha$ is the main driver for cost reduction, and the overlap penalty $\lambda$ effectively suppresses node congestion. The method provides a robust solution for managing change propagation in complex product design and enhances overall propagation efficiency and system adaptability. Full article

Passenger comfort in executive-class aircraft demands rigorous control of noise, vibration, and harshness. This study describes the development of a detailed, high-fidelity coupled structural–acoustic finite element model of the Piaggio P.180 passenger cabin, aimed at accurately predicting interior cabin noise within the low- to mid-frequency range. A hybrid discretization strategy was employed to balance computational efficiency and model fidelity. The fuselage structure was discretized using two-dimensional shell elements and one-dimensional beam elements, while the interior cabin air volume was represented using three-dimensional fluid elements. Mesh sizing in both the structural and acoustic domains were determined through analytical wavelength estimates and numerical convergence studies, ensuring appropriate resolution and accuracy. The model’s reliability and accuracy were validated through comprehensive modal analysis. The first three structural modes exhibited strong correlation with available experimental data, confirming the robustness of the numerical model. Subsequent harmonic response analyses were conducted to evaluate the intrinsic noise reduction characteristics of the P.180 airframe, specifically within the frequency range up to approximately 300 Hz. Full article

This paper presents a simulation study and development of a prototype of an integrated electronic system, specifically designed to enhance both the safety and the comfort of passengers and drivers in modern vehicles. The proposed system provides intelligent assistance during parking maneuvers by alerting the driver to nearby obstacles, and it also actively monitors the internal environment of the car cabin to detect the presence of harmful gases such as carbon monoxide, thereby improving occupant safety. In order to accomplish these objectives, a detailed functional algorithm was created and a corresponding structural scheme was designed. Simulation studies were carried out using the Tinkercad platform to validate the theoretical model and to test the behavior of the system components under realistic conditions. After a successful simulation, the physical prototype of the system was assembled and tested in a laboratory environment. The core of the system is based on the Arduino UNO microcontroller, which offers flexibility, ease of programming, and integration capabilities with various sensors and actuators. The study demonstrates the potential of low-cost microcontroller-based solutions for intelligent automotive systems focused on active safety and enhanced user experience. Full article

This study presents the development of an open-source Driver-in-the-Loop simulation platform, specifically designed to test and analyze advanced automated driving functions. We emphasize the creation of a versatile system architecture that ensures seamless integration and interchangeability of components, supporting diverse research needs. Central to the simulator’s configuration is a hexapod motion platform with six degrees of freedom, chosen through a detailed benchmarking process to ensure dynamic accuracy and fidelity. The simulator employs a half-vehicle cabin, providing an immersive environment where drivers can interact with authentic human–machine interfaces such as pedals, steering, and gear shifters. By projecting complex driving scenarios onto a curved screen, drivers engage with critical maneuvers in a controlled virtual environment. Key innovations include the integration of a motion cueing algorithm and an adaptable, cost-effective open-source framework, facilitating collaboration among researchers and industry experts. The platform enables standardized testing and offers a robust solution for the iterative development and validation of automated driving technologies. Functionality and effectiveness were validated through testing with the ISO lane change maneuver, affirming the simulator’s capabilities. Full article

This study presents an advanced simulation-based methodology for evaluating the dynamic vibrational behavior of the operator–vehicle assembly in autonomous electric agricultural tractors. Using the TE-0 electric tractor as the experimental platform, the research is structured into three integrated stages. In the first stage, a seated anthropometric virtual model of the human operator is developed based on experimental data and biomechanical validation. The second stage involves a detailed modal analysis of the TE-0 electric tractor using Altair Sim Solid, with the objective of determining the natural frequencies and vibration modes in the [0–80] Hz range, in compliance with ISO 2631-1. This analysis captures both the structural-induced frequencies—associated with the chassis, wheelbase, and metallic frame—and the operational-induced frequencies, influenced by the velocity and terrain profile. Subsequently, the modal analysis of the “Grammer Cabin Seat” is conducted to assess its dynamic response and identify critical vibration modes, highlighting how the seat behaves under vibrational stimuli from the tractor and terrain. The third stage extends the analysis to the virtual operator model seated on the tractor seat, investigating the biomechanical response of the human body and the operator–seat–vehicle interaction during simulated motion. Simulations were carried out using SolidWorks 2023 and Altair Sim Solid over a frequency range of [0–80] Hz, corresponding to operation on unprocessed soil covered with grass, at a constant forward speed of 7 km/h. The results reveal critical resonance modes and vibration transmission paths that may impact operator health, comfort, and system performance. The research contributes to the development of safer, more ergonomic, and sustainable autonomous agricultural transport systems. By simulating real-world operation scenarios and integrating a rigorously validated experimental protocol—including vibration data acquisition, biomechanical modeling, and multi-stage modal analysis—this study demonstrates the importance of advanced modeling in optimizing system-level performance, minimizing harmful vibrations, and supporting the transition toward resilient and eco-efficient electric tractor platforms in smart agricultural mobility. Full article

In this study, a multi-objective shape optimization framework was established for photovoltaic-powered underwater vehicles (PUVs) to systematically investigate multidisciplinary coupled design methodologies. Specifically, a global sensitivity analysis was conducted to identify four critical design parameters with 24 h energy consumption and cabin volume serving as dual optimization objectives. An integrated automated optimization workflow was constructed by incorporating parametric modeling, computational fluid dynamics (CFD) simulations, and dynamic surrogate models. Additionally, a new phased hybrid adaptive lower confidence bound (PHA-LCB) infill criterion was designed under the consideration of error-driven mechanisms, improvement feedback loops, and iterative attenuation factors to develop high-precision dynamic surrogate models. Coupled with the NSGA-II multi-objective genetic algorithm, this framework generated Pareto-optimal front solutions possessing significant engineering value. Furthermore, an optimal design configuration was ultimately determined through multi-criteria decision analysis. Compared to the initial form, it generates an additional 1148.12 Wh of electrical energy within 24 h, with an 22.36% increase in sailing range and a 2.77% improvement in cabin volume capacity. The proposed closed-loop “modeling–simulation–optimization” framework realized multi-objective optimization of PUV shape parameters, providing methodological paradigms and technical foundations for the engineering design of next-generation autonomous underwater vehicles. Full article

Accurately identifying driver fatigue in complex driving environments plays a crucial role in road traffic safety. To address the challenge of reduced fatigue detection accuracy in complex cabin environments caused by lighting variations, we propose YOLO-FDCL, a novel algorithm specifically designed for driver fatigue detection under complex lighting conditions. This algorithm introduces MobileNetV4 into the backbone network to enhance the model’s ability to extract fatigue-related features in complex driving environments while reducing the model’s parameter size. Additionally, by incorporating the concept of structural re-parameterization, RepFPN is introduced into the neck section of the algorithm to strengthen the network’s multi-scale feature fusion capabilities, further improving the model’s detection performance. Experimental results show that on the YAWDD dataset, compared to the baseline YOLOv8-S, precision increased from 97.4% to 98.8%, recall improved from 96.3% to 97.5%, mAP@0.5 increased from 98.0% to 98.8%, and mAP@0.5:0.95 increased from 92.4% to 94.2%. This algorithm has made significant progress in the task of fatigue detection under complex lighting conditions. At the same time, this model shows outstanding performance on our self-developed Complex Lighting Driving Fatigue Dataset (CLDFD), with precision and recall improving by 2.8% and 2.2%, respectively, and improvements of 3.1% and 3.6% in mAP@0.5 and mAP@0.5:0.95 compared to the baseline model, respectively. Full article

Facing the demands of Human–Robot Collaborative (HRC) assembly for complex products under Industry 5.0, this paper proposes an intelligent assembly method that integrates Large Language Model (LLM) reasoning with Augmented Reality (AR) interaction. To address issues such as poor visibility, difficulty in knowledge acquisition, and strong decision dependency in the assembly of complex aerospace products within confined spaces, an assembly task model and structured process information are constructed. Combined with a retrieval-augmented generation mechanism, the method realizes knowledge reasoning and optimization suggestion generation. An improved ORB-SLAM2 algorithm is applied to achieve virtual–real mapping and component tracking, further supporting the development of an enhanced visual interaction system. The proposed approach is validated through a typical aerospace electronic cabin assembly task, demonstrating significant improvements in assembly efficiency, quality, and human–robot interaction experience, thus providing effective support for intelligent HRC assembly. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

This paper introduces Planogen, a modular procedural generation plug-in for the Unity game engine, which is composed of two primary components: a character generation module (CharGen) and an airplane generation module (PlaneGen). Planogen facilitates the rapid generation of varied and interactive aircraft cabin environments populated with diverse virtual passengers. The presented system is intended for use in research experiment scenarios, particularly those targeting the fear of flying (FoF), where environmental variety and realism are essential for user immersion. Leveraging Unity’s extensibility and procedural content generation techniques, Planogen allows for flexible scene customization, randomization, and scalability in real time. We further validate the realism and user appeal of Planogen-generated cabins in a user study with 33 participants, who rate their immersion and satisfaction, demonstrating that Planogen produces believable and engaging virtual environments. The modular architecture supports asynchronous updates and future extensions to other VR domains. By enabling on-demand, repeatable, and customizable VR content, Planogen offers a practical tool for developers and researchers aiming to construct responsive, scenario-specific virtual environments that can be adapted to any research domain. Full article