Search Results (85)

Technological developments in transportation have moved quickly, often faster than the frameworks used to evaluate their broader societal and environmental implications. This study examines the extent to which Tesla’s automotive activities contribute to long-term societal well-being from a consequentialist utilitarian perspective, focusing on two related developments: (1) vehicles equipped with advanced driver assistance systems (ADAS) and (2) vehicles powered by electricity. Tesla provides a useful focal case in that it brings these two developments together in a single, highly visible setting. Using Tesla as an exploratory qualitative case, the analysis assesses both technologies within a single ethical and sustainability framework, examining how their effects combine across safety, environmental, and broader societal outcomes. Because the two technologies act on many of the same outcomes and stakeholders, they interact: they reinforce one another in some respects and offset one another in others. In the case of road safety, for example, the additional mass of an electric vehicle raises the severity of collisions even as driver assistance works to reduce their frequency. The analysis suggests an overall net positive societal impact, while recognizing the uncertainties and trade-offs that remain. This assessment rests mainly on two considerations: the likely reduction in traffic-related injuries and fatalities associated with wider adoption of ADAS-equipped vehicles, and the expectation that, in most contexts, electric vehicles provide a net environmental benefit, particularly through lower levels of harmful air pollutants relative to internal combustion engines. These benefits are not automatic, however, but depend on broader system conditions, including whether electrification and automation move transportation beyond established patterns of car dependence or reinforce them. The paper concludes by outlining the implications of these findings, while acknowledging the limits of the analysis and pointing to areas for future research. Full article

Ensuring adequate indoor air quality (IAQ) in industrial environments is essential for protecting worker health, particularly in facilities characterized by chemical emissions and complex layouts, such as automotive painting lines. This study presents the implementation and field evaluation of a low-cost multisensory electronic system prototype designed for continuous, long-term monitoring of six key environmental parameters: temperature, relative humidity, atmospheric pressure, carbon dioxide equivalent (CO₂ eq), total volatile organic compounds (VOC), and an overall Indoor Air Quality (IAQ) index. The system consists of autonomous sensing stations with integrated multi-parameter MEMS sensors and a centralized data aggregation hub. The system was engineered to ensure metrological stability across power cycles, adaptive energy management, and robust long-range wireless communication, thereby addressing common limitations of conventional industrial monitoring solutions. The prototype was deployed in an operational automotive manufacturing plant, where seven sensing stations were installed along the painting line for a two-week continuous monitoring campaign, identifying process-dependent peaks in CO₂ and VOC concentrations and corresponding reductions in IAQ values. The system was able to identify CO₂ peaks as high as 2997.7 ppm (Sensor 3) in localized industrial zones, significantly exceeding standard indoor thresholds. At the same time the system demonstrated the ability to detect VOC fluctuations with a resolution capable of capturing peaks up to 144.1 ppb (Sensor 3) during high-activity shifts. All sensors provided continuous and reliable data over an extended monitoring period. The measured trends and value ranges were consistent with expected industrial conditions, indicating satisfactory system performance under real operating conditions. Overall, the results demonstrate that the developed multisensory prototype is a promising, portable, and economically sustainable solution for distributed continuous IAQ assessment in complex industrial environments, with strong potential for scalable large-scale implementation in occupational health protection and environmental sustainability frameworks. Full article

In this study, the cooling performances of various nanofluids were compared under the operating conditions of a real automobile radiator, based on an internal combustion engine vehicle cooling system whose experiments had been previously completed. In the analyses, the radiator inlet fluid temperature was fixed at 70 °C, air inlet velocities were set to 6, 8, and 10 m/s, and fluid flow rates were taken as 17, 19, and 21 L/min. Under these conditions, the cooling capacities were evaluated for three different working fluids whose thermophysical properties were experimentally determined: 100% pure water, water-based 0.3% ZnO nanofluid, and water-based 0.3% ZnO + CuO hybrid nanofluid. Within the scope of this study, a Computational Fluid Dynamics (CFD) model was developed based on the aforementioned experimental parameters and validated with a maximum deviation of 6%. Using the validated model, additional CFD analyses were performed for water-based 0.3% Al₂O₃ and TiO₂ nanofluids, whose thermophysical properties were also experimentally determined, and their cooling performances were assessed. Based on the experimental and numerical results obtained, the highest cooling capacity was determined to be 20.8 kW in the 0.3% TiO₂ nanofluid, representing a 69.1% increase in cooling capacity compared to pure water. These findings clearly demonstrate that the use of nanofluids significantly enhances heat transfer performance in automotive cooling systems. Full article

A methodology for the development of nonlinear model predictive control for thermal management of a 40-ton fuel-cell heavy-duty truck is presented, using the medium-temperature cooling circuit as a case study. The approach integrates control-oriented modeling, parameter estimation, and experimental validation based on drivetrain test bench measurements under controlled high-temperature ambient conditions. A lumped-parameter model of the medium-temperature circuit, including coolant, oil, electric motors, and power-electronics auxiliaries, is derived and implemented in a Simulink environment, with heat-transfer parameters calibrated from test bench data and radiator air-side resistance and fan characteristics derived from CFD simulations and manufacturer specifications, respectively. Model parameters are identified using a systematic estimation procedure and the resulting model is validated against long-duration roller test measurements, achieving coefficients of determination above R² = 0.9 and normalized RMSE values below 10% for all key temperatures. The validated model is then used as the prediction model in a model predictive controller that manipulates radiator fan and coolant-pump speeds, while treating component heat losses, vehicle speed and ambient temperature as measured disturbances. The controller is evaluated in a model-in-the-loop environment for representative long-haul and urban driving cycles and different ambient temperatures, and its performance is benchmarked against conventional rule-based and PI-based control strategies. Depending on the driving cycle and ambient conditions, the proposed NMPC reduces cooling system energy consumption by up to 39.6% compared to a PI controller (VECTO Urban Delivery cycle, 35 °C ambient), with an average reduction of 16.6% across all investigated driving cycles and ambient conditions, without a significant increase in average or maximum coolant temperature. The proposed methodology provides a transferable workflow for developing predictive thermal management control in fuel-cell heavy-duty vehicles and other complex automotive cooling systems. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

The compressor in automotive air-conditioning systems consumes a significant fraction of the vehicle’s energy, thereby reducing driving range. Consequently, developing more efficient compressor operation is essential for improving overall thermal management. Nano-enhanced lubricants have emerged as a promising passive strategy to reduce compressor power consumption, enhance thermodynamic performance, and improve tribological behavior by minimizing friction and wear. This review critically examines existing nano-lubricant research with a focus on automotive compressor and system-level performance, friction and wear reduction mechanisms, and the influence of nanoparticle type and concentration on lubricant thermo-physical properties. The analysis reveals that nano-lubricants consistently enhance compressor operation by lowering discharge temperature and reducing power consumption, while improving coefficient of performance and cooling capacity. However, these benefits have been validated primarily under cooling-mode conditions and predominantly for reciprocating-piston compressors. Tribological studies further demonstrate substantial reductions in coefficient of friction and surface roughness, with improved anti-wear characteristics compared to virgin lubricants. Four principal mechanisms—rolling, polishing, protective-film formation, and self-repairing—have been identified as contributors to these enhancements. Nevertheless, most tribological investigations rely on simplified test rigs that do not fully represent the complex contact, loading, and thermal environments inside actual automotive compressors. This review underscores the need for system-level, mechanism-driven, and compressor-architecture-specific investigations covering both cooling and heating modes of automotive air-conditioning operation. The insights presented aim to guide future development of reliable, durable, and refrigerant-compatible nano-lubricant technologies for next-generation automotive air-conditioning systems. Full article

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

Thermo-physiological comfort of automotive seating is governed by the complex interaction between seat-cover materials, their structural configuration, and the heat and moisture exchange occurring at the seat–body interface during prolonged sitting. While numerous studies have examined individual textile constructions or isolated comfort parameters, integrated evaluations combining objective material testing with dynamic microclimate measurements under realistic loading conditions remain limited. This study thoroughly examined six commercially important vehicle seat-cover materials that represent laminated, warp-knitted, and woven polyester architectures. Standardized laboratory techniques were used to quantify objective comfort qualities, such as air permeability, water vapor permeability, thermal resistance (Rct), and evaporative resistance (Ret) and transient heat flux test (H-test). Simultaneously, a multi-sensor system was used to constantly monitor temperature and relative humidity at the seat–body interface during sitting loading in a controlled subjective microclimate experiment at room temperature. The findings show that lamination technique and textile structure have a major impact on both transient microclimate behavior and steady-state material properties. Increased air and moisture transmission in warp-knitted and more open structures resulted in reduced evaporative resistance and more stable microclimate conditions. Denser laminated structures, on the other hand, exhibited more resistance to heat and evaporation, which led to a greater buildup of moisture when they were seated. Different temporal responses in temperature and humidity were also shown by the multi-sensor microclimate studies, underscoring the significance of assessing comfort beyond static material metrics. This study demonstrates that static thermos-physiological parameters alone are not sufficient to predict real stated comfort behavior. By integrating time-resolved microclimate analysis under realistic seated loading with standardized testing, a more reliable evaluation framework for automotive seat-cover comfort is proposed. Full article

This review presents a modern comprehensive analysis of the physicochemical characteristics of carbon materials for the sorption of gases. The main classes of carbon sorbents are considered as follows: nanotubes, activated carbon, graphene, fullerene, composite materials, and organic vapors. Modern methods of modification of carbon materials are systematized. Particular attention is paid to the effect of particle size, morphology, and porous structure on the kinetics and equilibrium characteristics of adsorption. The results of experimental and theoretical studies of the adsorption of the gases (CO₂, SO₂, NO_x, H₂S, NH₃, and CO) are analyzed. A comparative economic analysis of carbon materials is carried out, taking into account the cost of production and estimated costs of modification. Modern areas of application of carbon sorbents are analyzed as follows: industrial gas purification, automotive filters, air conditioning systems, personal protective equipment, and gas sensors. Particular attention is paid to the study of the prospects and future of materials. Prospective development directions are considered, including the creation of hierarchically porous structures, the development of self-healing materials, and integration with artificial intelligence to optimize adsorption processes. The cost of graphene and nanotube production is predicted to decrease by 50–70% by 2030, which will lead to an expansion of their commercial application. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) systems in electric vehicles significantly penalize the driving range due to high power consumption, particularly during heating operation. While R744 heat pump systems are gaining traction as a promising solution for addressing this limitation, their ventilation strategies pose a complex trade-off between energy consumption and in-vehicle air quality (IAQ). Specifically, the continuous use of recirculation mode to enhance energy efficiency can lead to the accumulation of occupant-exhaled CO₂ conversely, introducing fresh air to dilute CO₂ results in both an energy penalty and the infiltration of external pollutants. To analyze this complex trade-off under varying fresh-to-recirculated air ratios, this study developed a model of an automotive R744 HVAC system and IAQ considering CO₂ and PM2.5 using the 1D simulation tool AMEsim. The system model was validated against experimental data from the literature, demonstrating high fidelity with overall relative errors consistently within 10%. The analysis of the ventilation strategy reveals a distinct IAQ trade-off: increased fresh air intake effectively diluted in-cabin CO₂ concentrations but simultaneously increases the infiltration of external PM2.5. In terms of energy efficiency, during cooling mode, increased fresh air intake reduces the system $COP$ from 1.77 to 1.54 and increases total battery consumption by up to 57%. Conversely, in heating mode, the increased fresh air intake enhances heat exchanger efficiency, leading to a rise in the COP by up to 9%. However, due to the higher overall thermal load, the total battery consumption still increases by up to 24%. Full article

Persistent discrepancies remain in the perceived far-field noise of automotive permanent-magnet synchronous motors (PMSMs) and the predictions of conventional NVH simulations. To bridge this gap, a Tri-source Electromagnetic Coupling NVH Integrated Framework (Tri-ECNVH) is developed, in which air-gap electromagnetic force harmonics, torque ripple, and cogging torque are treated as a coupled excitation system rather than as independent sources. Traditional workflows usually superpose their responses in the power domain, which tends to underestimate the radiating contribution of torque-related excitations and neglect their phase and order coupling with radial electromagnetic forces. In the proposed Tri-ECNVH framework, the three sources are mapped into the order domain, aligned by spatial order, and applied to the stator with phase consistency, so that inter-source coupling and cross terms are explicitly retained along a unified electromagnetic–structural–acoustic chain. Acoustic radiation is evaluated by prescribing the normal velocity on the stator outer surface as a Neumann boundary condition and computing the far-field A-weighted sound pressure level (SPL) using a boundary element method (BEM) model. Numerical results reveal pronounced cooperative amplification of the three sources at critical orders and within perceptually sensitive frequency bands; relative to independent-source modeling with power-domain summation, Tri-ECNVH predicts peak levels that are typically 5–10 dB higher and reproduces the spectral envelope and peak–valley evolution more faithfully. The framework therefore offers a practical, radiation-oriented basis for multi-source noise mitigation in traction PMSMs and helps narrow the gap between simulation and perceived sound quality in automotive applications. Full article

Air cycle systems, once largely replaced by vapour-compression technologies due to efficiency concerns, are now re-emerging as a viable and sustainable alternative for highly dynamic thermal applications and excel in ultra-low temperature. By using air as the working fluid, these systems eliminate the need for synthetic refrigerants and comply naturally with evolving environmental regulations. This study presents the conceptual design and simulation-based analysis of a novel air cycle machine developed for advanced automotive testing environments. The system is intended to replicate a wide range of climatic conditions—from deep winter to peak summer—through the use of fast-responding turbomachinery and a flexible control strategy. A central focus is placed on the radial turbine, which is designed and evaluated using a modular, open source framework that integrates geometry generation, off-design CFD simulation, and performance mapping. The study outlines a potential operating strategy based on these simulations and discusses a control architecture combining lookup tables with zone-specific PID tuning. While the results are theoretical, they demonstrate the feasibility and flexibility of the proposed approach, particularly the turbine’s role within the system. Full article

The liquid reservoir is a critical component of the automotive air conditioning system, while weld seams on its surface may exhibit different types of defects with various shapes and scales, meaning traditional detection methods struggle to detect them effectively. In this article, we propose a YOLOv8s-based algorithm to detect liquid reservoir weld defects. In order to improve feature fusion within the neck and enhance the model’s capacity to detect defects showing substantial size variations, the neck is optimized through the integration of the improved Reparameterized Generalized Feature Pyramid Network (RepGFPN) and the addition of a small-object detection head. To further improve the capacity of identifying complex defects, the Spatial Pyramid Pooling Fast (SPPF) module in YOLOv8s is substituted with Focal Modulation Networks (FocalNets). Additionally, the Cascaded Group Attention (CGA) mechanism is incorporated into the improved neck to minimize the propagation of redundant feature information. Experimental results indicate that the improved YOLOv8s achieves a 6.3% improvement in mAP@0.5 and a 4.3% improvement in mAP@0.5:0.95 compared to the original model. The AP value for detecting craters, porosity, undercuts, and lack of fusion defects improves by 3.9%, 13.5%, 5.0%, and 2.5%, respectively. We conducted comparative experiments against other state-of-the-art models on the liquid reservoir weld dataset and the steel pipe weld defect dataset, and the results show that our model has outstanding detection performance. Full article

Air compressors are vital across various sectors—automotive, manufacturing, buildings, and healthcare—as they provide pressurized air for air suspension systems in vehicles, supply power pneumatic machines throughout industrial production lines, and support non-clinical infrastructure within hospital environments, including pneumatic control systems, isolation room pressurization, and laboratory equipment operation. Ensuring that such components are reliable is critical, as unexpected failures can disrupt facility functions and compromise patient safety. Predictive maintenance (PdM) has emerged as a key factor in enhancing the reliability and operational efficiency of medical devices by leveraging sensor data and artificial intelligence (AI)-based algorithms to detect component degradation before functional failures occur. In this paper, a predictive maintenance solution for condition monitoring and fault prediction for the exhaust valve, bearings, water pump, and radiator of an air compressor is presented, by comparing a hybrid deep neural network (DNN) as a feature extractor and a support vector machine (SVM) for condition classification: a pure DNN classifier as well as a standalone SVM model. Additionally, each model was trained and validated on three devices—NVIDIA T4 GPU, Raspberry Pi 4 Model B, and NVIDIA Jetson Nano—and performance reports in terms of latency, energy consumption, and CO₂ emissions are presented. Moreover, three model agnostic explainable AI (XAI) methods were employed to increase the transparency of the hybrid model’s final decision: Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDP). The hybrid model achieves on average 98.71%, 99.25%, 98.78%, and 99.01% performance in terms of accuracy, precision, recall, and F1-score across all devices Additionally, the DNN baseline and SVM model achieve on average 93.2%, 88.33%, 90.45%, and 89.37%, as well as 93.34%, 88.11%, 95. 41%, and 91.62% performance in terms of accuracy, precision, recall, and F1-score across all devices. The integration of XAI methods within the PdM pipeline offers enhanced transparency, interpretability, and trustworthiness of predictive outcomes, thereby facilitating informed decision-making among maintenance personnel. Full article

Effective thermal management is crucial for optimizing the performance, efficiency, and durability of fuel-cell technologies, including proton-exchange membrane fuel cells (PEMFCs) and solid-oxide fuel cells (SOFCs). The operation of fuel cells involves complex heat generation mechanisms, primarily driven by electrochemical reactions, which can lead to significant energy loss as heat. This review examines the specific heat generation sources and challenges associated with different fuel-cell types, highlighting the critical importance of effective thermal management strategies. Key techniques for thermal regulation, including active and passive cooling systems, are examined in detail. Active cooling methods like liquid cooling and air cooling are effective in dissipating excess heat, while passive methods leverage advanced materials and optimized designs to enhance natural heat dissipation. Furthermore, innovative heat recovery systems are explored, demonstrating their potential to enhance overall energy efficiency by capturing and repurposing waste heat. The integration of machine learning techniques has arisen as a promising avenue for advancing temperature control in fuel cells. Reinforcement learning, deep learning algorithms, and support vector machines, along with artificial neural networks, are discussed in the context of their application in managing temperature dynamics and optimizing thermal performance. The review also emphasizes the significance of real-time monitoring, as well as adaptive control strategies to respond effectively to the dynamic operating conditions of fuel cells. Understanding and applying these thermal management strategies is essential for the successful commercialization of fuel cells across various sectors, ranging from automotive to stationary power generation. With the growing demand for clean energy solutions, progress in thermal management techniques will be crucial in improving the dependability and practicality of fuel-cell systems. Full article