Abstract: The objective of the article is to present investigates the feasibility of driver-state assessment in a real automotive environment using a mobile long-wave infrared (LWIR) thermal camera. Unlike visible-spectrum systems, thermal imaging provides illumination-invariant and temperature-dependent information that is particularly advantageous inside a vehicle, where lighting conditions vary substantially. A handheld microbolometer (UTi260M) was used to record thermal video of a driver during prolonged, monotonous driving with a stabilized cabin temperature. Pixel-wise temperature reconstruction, spatial noise estimation, uniformity analysis, and NETD approximation were applied to evaluate thermal image quality and to quantify thermophysiological changes associated with drowsiness. The thermal recordings revealed characteristic pre-sleep markers, including head droop, reduced neuromuscular correction, elevated and spatially uniform facial temperature, and diminished thermal variability. These patterns correspond to known physiological responses to fatigue, reduced sympathetic activation, and warm cabin exposure. The analysis demonstrates that mobile thermal imaging can reliably capture early indicators of declining vigilance and can support the development of non-contact driver-monitoring systems. The findings further suggest that integrating temperature-driven alerting thresholds into mobile applications may provide an additional preventive mechanism against drowsiness-related accidents.

Abstract: Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO2, and O2, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow.

Abstract: This paper introduces a novel Magnetorheological (MR) damper integrating a ball-screw mechanism (SMRB damper), designed to unify translational and rotational motion for enhanced automotive suspension performance. While shear-mode rotary MR dampers offer excellent responsiveness and stability, prior designs face persistent issues such as high off-state torque, structural complexity, or limited damping force. The proposed damper aims to overcome these limitations. Its design and operating principle are presented, followed by the development of a mathematical model based on the Bingham-plastic formulation and finite element analysis. To maximize damping capability, the key structural parameters are optimized using an Adaptive Particle Swarm Optimization (APSO) algorithm. Finally, a prototype is fabricated based on the optimized results, and experimental tests validate its performance against simulation predictions, demonstrating its improved potential for vibration control applications.

Abstract: Biodiesel fuel produced through transesterification is mainly used in blends with conventional diesel fuel. The analysis of combustion process parameters for each specific biodiesel fuel represents the basis for a rational approach to the utilization of available motor fuel quantities. In this study, the heat release rate and cumulative heat release during the combustion of conventional diesel fuel and blends of biodiesel fuel made from waste grape seed oil and conventional diesel fuel were analyzed. The tests were conducted on a single-cylinder, air-cooled diesel engine with direct fuel injection. The combustion of conventional diesel fuel, a blend containing 7% of biodiesel by volume (B7), and a blend containing 14% of biodiesel by volume (B14) was examined. Using blends, especially those with a higher biodiesel content (B14), results in a higher maximum heat release rate compared to conventional diesel fuel, which can have negative implications in terms of mechanical stresses and engine noise. However, the higher combustion rate of the B14 blend, particularly during the combustion of the first 50% of the fuel mass per cycle, can have a positive impact on the fuel economy of the working cycle and the engine as a whole.

Abstract: Motor position sensors are critical parts for traction motors control in electrified automotive powertrain. As motors are getting more compact due to the advance of technology the packaging space for motor position sensors is becoming increasingly restricted. This study presents a two-layer (2L) printed circuit board (PCB) routing strategy for inductive motor position sensors with limited area. A prototype was fabricated and tested on a test bench using a comprehensive design of experiments that contains 625 combinations of X- and Y-offsets, tilt angle, and airgap at various levels (±0.5 mm in X/Y, ±0.5° tilt, 1.9–3.1 mm airgap). Across the tolerance box, the accuracy under all test cases remained within ±1 electrical degree. The accuracy analysis through Fourier series on circle shows that the DC offset and magnitudes mismatch of the 3 Rx signals are the dominant error contributors due to the routing modification. An Extreme Gradient Boosting (XGBoost) model was trained and validated with R² = 0.9951 on the sensor data. The SHapley Additive exPlanations (SHAP) analysis identified tilt and Y-offset as dominant contributors to accuracy degradation. The model revealed a mild Y-axis asymmetry introduced by routing modifications. The SHAP results show that machine learning workflow provides a general, quantitative framework for analyzing inductive sensor layouts and installation tolerances.

Abstract: Fuel consumption in heavy-duty off-road machinery depends on a wide range of in-teracting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parame-ters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption As-sessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term opera-tional data collected during real industrial work. Its universal structure makes it applica-ble not only to mining equipment, but also to construction machinery and transport vehi-cles, as well as conventional passenger cars, where it offers a systematic procedure for es-timating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency.

Abstract: Transmission error (TE) is one of the most important sources of gear noise and vibration. Manufacturing tolerances and assembly shifts introduce deviations in tooth geometry that produce periodic mesh disturbances; these disturbances excite the drivetrain and radiate as airborne sound. While many studies have modelled individual tolerance effects in deterministic simulations, few have evaluated the combined influence of realistic tolerance distributions using open Monte–Carlo data. This work analyses the public Gear Statistical tolerance analysis dataset (≈40 k samples) to answer the following questions: (Q1) How accurately can the kinematic transmission error be predicted from measured tolerance and process‑shift data? (Q2) Which individual tolerances and their interactions have the greatest impact on TE? (Q3) Can a TE‑derived proxy quantify noise‑critical excitation without an acoustic model? (Q4) What tolerance combinations minimise TE and noise while respecting manufacturing cost? (Q5) Is there a quantifiable trade‑off between tolerance tightening cost and noise reduction? To address these questions we formulate hypotheses: H1 Non‑linear machine‑learning models achieve high predictive accuracy for TE (R² > 0.85) compared with linear baselines. H2 A small subset of tolerances—profile and lead errors—dominate the TE variance and interact non‑linearly. H3 A relative noise proxy (RNP) derived from TE faithfully ranks noise‑critical excitation across tolerance combinations. H4 A Pareto front exists in the cost–noise plane, enabling cost‑effective tolerance optimisation. H5 Targeted adjustment of the top two tolerances yields larger noise‑reduction per cost than uniform tightening. The following sections describe the data, the modelling framework and the results that support these hypotheses.

Abstract: Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety.This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab's X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems.

Abstract: Radiated noise from gearbox housings is a significant contributor to the noise, vibration, and harshness (NVH) performance of automotive drivetrains. Small deviations in wall thickness caused by standard manufacturing tolerances can influence structural stiffness, shift modal frequencies, and alter acoustic radiation levels, particularly near resonance. This study numerically examines the effect of ±10% wall thickness variation using a simplified multi-mode structural–acoustic model. Modal frequencies were scaled proportionally to thickness changes, and frequency response functions were calculated for nominal and tolerance-limit geometries. Statistical variability was assessed through Monte Carlo simulations with wall thickness values sampled from a truncated normal distribution within the tolerance range. Results show that a ±10% variation can produce modal frequency shifts accompanied by resonance amplitude differences of up to 20.9 dB in the 950–1050 Hz range, with an average change of 3.34 dB between extreme cases. Monte Carlo analysis indicated mean changes of 0.54 dB in the main resonance band, with some cases reaching 2.54 dB. The findings demonstrate that even within standard production limits, wall thickness tolerances can measurably affect gearbox NVH behavior. Considering such variability in early-stage design can help reduce unit-to-unit noise differences and improve acoustic robustness.

Abstract: Accurate detection and localization of traffic objects are essential for autonomous driving tasks such as path planning. While semantic segmentation is able to provide pixel-level classification, existing networks often fail under challenging conditions like nighttime or rain. In this paper, we introduce a new training framework that combines unsupervised domain adaptation with high dynamic range imaging. The proposed network uses labeled daytime images along with unlabeled nighttime HDR images. By utilizing the fine details typically lost in conventional SDR images due to dynamic range compression, and incorporating the UDA training strategy, the framework effectively trains a model which is capable of semantic segmentation across the adverse weather conditions. Experiments conducted on four datasets have demonstrated substantial improvements in inference performance under nighttime and rainy scenarios. The accuracy for daytime images is also enhanced through expanded training diversity. Source code is available at https://github.com/ZackChen1140/RMSeg-HDR.

Abstract: Income tax fraud is a serious challenge to revenue authorities, which involves large amounts of money losses and erodes public trust. The conventional methods of detection through manual audits and rule-based systems tend to be slow and ineffective for large volumes of data. This study suggests an AI - and ML-based framework for the detection of tax fraud through fraudulent returns by using both supervised and unsupervised learning algorithms. Major models adopted are Random Forest, XG-Boost, and Isolation Forest, selected based on their performance in classification and outlier detection. Feature engineering is directed toward significant features like income patterns, deductions, exemptions, and past filing behavior to detect abnormal patterns that signify fraud. Experimental outcomes reveal that Random Forest performed best with an accuracy of 96%, followed by XG-Boost with an accuracy of 95%, and Isolation Forest with an accuracy of 80%, showing the best performance of tree-based ensemble models for the task. The system provides a risk score for every tax return, allowing authorities to rank audits and reduce false positives. These results show that machine learning models far surpass conventional methods, delivering a scalable and automated solution for effective fraud detection. The research provides a realistic basis for incorporating AI-based strategies in financial fraud management, leading to increased compliance, minimized revenue leakage, and better decision-making by tax authorities.

Abstract: Lightweight gearbox housings often raise NVH risk, yet full finite-element evaluations are too slow for early design screening. This study tests whether a few frequency-band descriptors of radiated sound are enough to classify housing stiffness. Using an open dataset of electric-vehicle gearbox spectra for three rib-configurations—flexible, intermediate and rigid—we averaged sound-pressure levels in five 1 kHz bands. Principal-component analysis separated the twelve samples into three non-overlapping groups, confirmed by k-means clustering (adjusted Rand index = 1.00). The random-forest model achieved 75 % classification accuracy on the present 12-sample data set (leave-one-out evaluation). Owing to the small sample size this figure should be regarded as explorative, and a larger validation study is required to confirm generalizability; permutation analysis confirmed the 3–4 kHz and 2–3 kHz bands as most important for classification. In contrast, total integrated spectral energy showed no significant group difference (p = 0.81). The results These findings suggest that mid-frequency band energy may encode structural-stiffness differences, although validation on larger datasets is necessary. The workflow—load spectra, compute five band means, classify—offers a rapid, interpretable tool for NVH-aware lightweight design.

Abstract: With air resistance being one of the two major energy losses in on-road vehicles (the other one being tire losses) and therefore heavily contributing to the range of battery-electric and fuel cell-electric vehicles, it is necessary to account for realistic air resistance in a-priori assessments like vehicle range esti-mations, component dimensioning, and system simulations. However, lack of input data tempts analysts to instead assume unrealistic “nom-inal conditions” throughout – a simplification which usually underestimates the amount of energy actually required to overcome air resistance and completely ignores the fact that varying environmental conditions will lead to significant variances in energy consumption and therefore vehicle range. Using “nominal conditions” it is thus impossible to assess the robustness of these measures and therefore difficult to design robust systems and to perform meaningful trade-off studies. In this study we show how publicly available data from weather observations can be used to assess the long-term variation of air resistance of a truck with semitrailer. Realistic distributions of energy losses due to air resistance, covering multiple years are derived – showing not only average values but the complete envelope in which the energy losses vary. This, in turn, enables to follow up with probabilistic calculations of vehicle per-formance in order to assess robustness and trade-offs on various system levels of interest. As a consequence consumption and range predictions of EVs and ICE vehicles can be performed with higher accuracy and confidence.

Abstract:

The operating conditions of engines in motor vehicles used in conditions of high air dustiness resulting from sandy ground and in helicopters using temporary landing sites were analyzed. The impact of mineral dust on accelerated abrasive and erosive wear of components and assemblies of piston and turbine engines was presented. Attention was drawn to the formation of dust deposits on turbine engine components. The possibilities of minimizing abrasive wear by using two-stage intake air filtration systems in motor vehicle engines were presented. The filtration properties of cyclones used as the first stage of air filtration were discussed. Three forms of protection for helicopter engines against the intake of contaminated air and to extend their service life were presented: intake barrier filters (IBF), tube separators (VTS), and particulate separators (IPS) called Engine Air Particle Separation (EAPS). It has been shown that pleating the filter bed significantly increases the filtration area without increasing the frontal area, whereby optimization of the filter bed geometry is of great importance here. An important advantage of the VTS air filtration system was demonstrated in the form of no maintenance due to the use of a system for the continuous removal of separated dust, whereby increasing the suction flow increases separation efficiency and pressure drop and energy losses. IPS is an air filtration system integrated with a turbine engine, characterized by a compact design, low external resistance, and no periodic maintenance, but with lower separation efficiency than VTS and IBF systems. The primary goal of such systems is to separate as many solid particles as possible at the lowest possible energy cost. The results of experimental research conducted by the author are presented, the aim of which was to demonstrate the advantages of a filtration unit consisting of cyclones and a porous partition in terms of increased filtration efficiency and filter operating time. During the research, an innovative method was used to determine the characteristics of a barrier filter operating in a two-stage filtration system, which reduces testing time and energy losses. It was a single VTS axial flow cyclone with a test barrier filter arranged in series behind it, whose filter bed was pleated paper with a suitably selected surface area. The results confirmed the advisability of using two-stage filtration systems to purify the intake air for internal combustion engines of motor vehicles and helicopter turbine engines. Since a porous partition increases pressure drop during operation, it is advisable to use permissible resistance sensors to limit their use due to increasing engine energy losses.

Abstract: This study presents the design, real-time implementation, and full-scale experimental validation of a novel rule-based Energy Management Strategy (EMS) for a dual-stack Fuel Cell Hybrid Electric Vehicle (FCHEV) developed on a Jeep Wrangler platform. Unlike previous studies limited to simulations or single-stack configurations, this work experimentally demonstrates, for the first time, a deterministic real-time EMS for a dual fuel cell system in an SUV class vehicle. The control algorithm, deployed on a National Instruments CompactRIO embedded controller, ensures real-time energy distribution and stable hybrid operation under dynamic load conditions. Simulation analysis performed over eight consecutive WLTC cycles confirmed that both fuel cell stacks operated within their optimal efficiency range (25–35 kW), achieving an average DC efficiency of 68% and a hydrogen consumption of 1.35 kg/100 km. Experimental validation on the Wrangler FCHEV demonstrator yielded an equivalent hydrogen consumption of 1.67 kg/100 km, corresponding to 1.03 kg/100 km·m² after aerodynamic normalization (Cd·A = 1.624 m²), comparable to commercial fuel cell vehicles. The results demonstrate that the proposed EMS maintains over 80% of operating time within high-efficiency zones, reducing fuel cell cycling and enhancing durability. This work bridges the gap between theoretical EMS design and real-world embedded validation, establishing a scalable benchmark for future hydrogen powertrain control systems.

Abstract: Auxiliary systems (HVAC, thermal management) significantly impact the range of electric vehicles under varying weather conditions. This study developed an XGBoost machine learning framework to predict auxiliary power using 95,028 real-world measurements of electric vehicles in Poland, Italy, and Germany (−8°C to +33.5°C). A physics-based energy decomposition combined with feature engineering achieved R² = 0.9986 with a mean ab-solute error of 35 W. The feature importance analysis revealed the position of the accelera-tor pedal (0.4153) as the strongest predictor, alongside the heating efficiency per tempera-ture differential (0.2716), indicating the coupling between traction demand and auxiliary loads. Heating dominated cooling by a 7:1 ratio with a 44-fold power variation in the temperature range. The contribution of the additional power ranged from 75% during idle to 12% during highway driving, contradicting static overhead assumptions. Results demonstrate that auxiliary loads require context-aware prediction rather than fixed as-sumptions, enabling improved range forecasting for electric vehicles.

Abstract: This work provides an updated report on the progress and prospects of quantum computing for advancing transport modeling. Despite recent developments in classical methods, their use in computationally intensive transport tasks such as traffic assignment, traffic signal control, logistics, and pedestrian modeling remains limited due to the combinatorial complexity and uncertainty associated with the real-world transport scenarios. Quantum optimization algorithms offer the potential for addressing transport problems, although most studies still remain at the proof-of-concept stage. In this work, we highlight key challenges in traffic and logistics optimization and investigate how quantum optimization tools could lead to novel and efficient solutions. Our perspective offers a clear and concise timeline for the development of quantum optimization techniques and their practical implementation aspects by highlighting key hardware/software challenges and proposed solutions. We also discuss the barriers to adoption within transport contexts and possible pathways to overcome current limitations to the application of quantum optimization techniques in real-world transport systems. Finally, we provide our perspective for the applications of quantum computing in addressing challenges pertaining to large crowd management and transport optimization during the Olympics and Paralympics 2032 Games which may coincide with the development of a large-scale quantum computer.

Abstract: A non-pneumatic tyre (hereby referred to as “NPT”) is a tyre that is not supported by any internal air pressure. Hence, they tend to be more reliable than a conventional tyre supported by compressed air. They have a wide range of advantages mainly due to their run-flat properties and low rolling resistance. While pneumatic tyres are still preferred, it has displayed some disadvantages such as the necessity to maintain interior air pressure and the possibility to burst while driving. Recently, non-pneumatic tyres have been developed in the hope that they may replace conventional tyres one day. However, most of the research conducted is mainly based on spokes with a uniform degree. In this paper, we discuss the behaviour of a non-pneumatic tyre with a varying degree of honeycombed structured spokes. The density of spokes will decrease when approaching the surface of the tyre which is on contact with the road, and increases when approaching the centre hub of the wheel. The new design was modelled on Autodesk Inventor and analysed on Ansys Workbench to obtain results similar to what we expected. By altering the distance and shape between the spokes, it has displayed significant improvement over existing NPT’s showing less stress and deformation in the wheel.

Abstract: Urban air pollution and emission reduction commitments have stimulated interest in cleaner vehicle technologies in Latin America, yet hybrid vehicle penetration in Ecuador, particularly in Guayaquil, remains limited. This study analyzes technical and commercial determinants of purchase intention using a mixed methods design that combines a survey of 384 consumers with interviews of 20 dealership representatives. Spearman correlations indicate that technical attributes show stronger associations with purchase intention than commercial variables: technology and performance (ρ = 0.65, p < 0.001) and maintenance (ρ = 0.61, p < 0.001) are the most influential, followed by environmental influence (ρ = 0.53, p < 0.001); public policies (ρ = 0.48, p < 0.001) and purchase price (ρ = 0.45, p < 0.001) display moderate effects. Overall, 51.5% of respondents report a favorable intention to purchase a hybrid vehicle in the short to medium term. Interviews confirm an information gap on tax incentives at the point of sale and underscore the potential of financing schemes to mitigate upfront cost barriers. Findings suggest that, in this market, narratives emphasizing long term operating savings and reliability outperform purely environmental appeals. We discuss implications for dealership communication, targeted credit programs, and public awareness campaigns to accelerate sustainable mobility transitions in urban Ecuador.

Abstract: Modern automotive design has increasingly embraced plastics for bumper construction, however it can lead to material degradation. To overcome these limitations, the automotive industry is turning to fiber‑resin material, namely carbon-epoxy composites. Our research focuses on knowing layer thickness and orientation angles effect into structural stress of car bumper, examining the combinations of material into the impact strength, and performing impact tests in various speeds. To build research gaps, machine learning (ML) has an important position to predict the certain value of parameters and measure data normality through various algorithms. By combining ML and FEA simulations, the result shows strong data performance. Bridging from 3 mm mesh sizing of 100% carbon-epoxy woven laminate in 6 mm thickness at 0° orientation shows the most distributed von Mises stresses which converged toward stable values. The elevated impact velocities improve stress magnitudes at 10.247 MPa, 16.857 MPa, and 23.438 MPa in 11.11 m/s, 15.61 m/s, and 22.22 m/s, respectively. For comprehensive research, total deformation was included in ML analysis as second targets to build multivariate analysis. Overall, Random Forest (RF) shows as the best model which indicates MSE (0.18) and R² (0.79), indicating superior robustness when modeling equivalent stress and total deformation.