Search Results (193)

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article

U.S. gasoline and diesel prices are often volatile, driven by geopolitical risks and disruptions in the fossil fuel market. Forest biomass fuels, particularly renewable diesel derived from logging residues, offer a low-carbon alternative with the potential to stabilize fuel prices. This study evaluates whether biomass can moderate fuel price volatility using ANOVA, Tukey post hoc tests, and quadratic regression based on monthly data for biomass production, inventories, and retail fuel prices. Findings reveal the existence of a significant nonlinear relationship between forest biomass inventory levels and fossil fuel prices. Average gasoline prices peaked in the medium-inventory group (M = 0.837) and dropped in the high-inventory group (M = 0.684). Diesel prices followed a similar pattern, with the highest values in the medium-inventory group (M = 0.963) and the lowest in the high-inventory group (M = 0.759). One-way ANOVA results were statistically significant for both gasoline (F(2, 99) = 7.39, p = 0.001) and diesel (F(2, 99) = 7.22, p = 0.0012). Tukey tests confirmed that diesel prices fell significantly from both medium to high and low to high-inventory levels. This result remains robust when using the biomass index level and the biomass production level. These results indicate a threshold effect: only at higher biomass inventories do fossil fuel prices decline, suggesting a potential for substitution. However, current policies inadequately support biomass integration, highlighting the need for targeted reforms. Full article

Short fiber-reinforced polymer composites are extensively used in automotive structural components, such as engine mounts and motor mount brackets, due to their favorable strength-to-weight ratio. For motor mount brackets, accurate structural analysis requires consideration of fiber orientation, as it significantly affects the mechanical behavior of the composite. This study aims to investigate the influence of fiber orientation heterogeneity on the mechanical properties of short fiber-reinforced polymer composites formed by injection molding. The spatial variation of the fiber orientation tensor, which evolves from the gate to the flow end during molding, presents challenges in experimental characterization. To address this, microscale analysis was conducted using injection-molded tensile specimens, followed by mesoscale modeling through representative volume elements (RVEs). Homogenization techniques were applied to predict effective mechanical properties, which were subsequently used to evaluate the performance of actual components at the macroscale. The findings demonstrate the importance of multi-scale modeling in capturing the anisotropic behavior of fiber-reinforced composites and provide a framework for more reliable structural analysis in automotive applications. Full article

Deep groove ball bearings are important mechanical elements in the automotive and process industries, particularly in electric motors. One of the primary reasons for their failure is lubricant degradation due to stray shaft current. Thus, the present work exhibited the failure of bearings under simulated lubricated conditions similar to those of real time bearings failing in presence of stray electric current. The test was conducted using a full bearing test rig with an applied radial load, 496 N, an alternating current, 10 A, and a rotation of 2000 rpm for 24 h. The bearings (6206 series) were greased using two commercially available ester-polyalphaolefin oil-based greases with viscosity 46–54 cSt (Grease 1) and 32–35 cSt (Grease 2, also contained aromatic oil). The optical microscopic images of the bearing raceways after the tribo test indicated the superior performance of Grease 1 compared to Grease 2, with lesser formation of white etching areas, micro-pitting, spot welds, and fluting on the surfaces of the bearings. Additionally, 80% less vibrations were recorded during the test with Grease 1, indicating a stable lubricating film of Grease 1 during the test as compared to Grease 2. Furthermore, a higher extent of Grease 2 degradation during the tribo test was also confirmed using Fourier transform infrared spectroscopy. Statistical analysis (t-test) indicated the significant variation of the vibrations produced during the test with electrified conditions. The present work indicated that the composition of the greases plays a significant role in controlling the bearing failures. Full article

This study investigates the low-signal behavior of electric motors in automotive applications, emphasizing impedance measurement, evaluation, and the definition of a simplified dummy load. A comprehensive experimental analysis was conducted on two induction motors with different power ratings (300 W and 45 kW), exploring the influence of winding topology, rotor position, and excitation amplitude on the impedance response. A simplified equivalent circuit model (ECM), derived solely from terminal impedance measurements, was developed and validated to construct a practical dummy load. This model facilitates realistic simulations without requiring detailed internal motor specifications. Experimental results confirm that the dummy load accurately replicates the measured impedance characteristics in the low-to-mid frequency range, demonstrating its effectiveness for electromagnetic interference (EMI) prediction and system-level simulations in automotive electric drive system. Full article

Pupil detection is a key technology in fields such as human–computer interaction, fatigue driving detection, and medical diagnosis. Existing pupil detection algorithms still face challenges in maintaining robustness under variable lighting conditions and occlusion scenarios. In this paper, we propose a novel pupil detection algorithm, ViMSA, based on the ViM model. This algorithm introduces weighted feature fusion, aiming to enable the model to adaptively learn the contribution of different feature patches to the pupil detection results; combines ViM with the MSA (multi-head self-attention) mechanism), aiming to integrate global features and improve the accuracy and robustness of pupil detection; and uses FFT (Fast Fourier Transform) to convert the time-domain vector outer product in MSA into a frequency–domain dot product, in order to reduce the computational complexity of the model and improve the detection efficiency of the model. ViMSA was trained and tested on nearly 135,000 pupil images from 30 different datasets, demonstrating exceptional generalization capability. The experimental results demonstrate that the proposed ViMSA achieves 99.6% detection accuracy at five pixels with an RMSE of 1.67 pixels and a processing speed exceeding 100 FPS, meeting real-time monitoring requirements for various applications including operation under variable and uneven lighting conditions, assistive technology (enabling communication with neuro-motor disorder patients through pupil recognition), computer gaming, and automotive industry applications (enhancing traffic safety by monitoring drivers’ cognitive states). Full article

Rapid progress in lithium-ion batteries and AI-powered autonomous driving is poised to propel electric vehicles to a 50% share of the global automotive market by the year 2035. Today, there is a major focus on recycling electric vehicle motors, particularly on extracting rare earth elements (REEs) from NdFeB permanent magnets (PMs). This research is based on a single-furnace process concept designed to separate metal components within PM motors by exploiting the varying melting points of the constituent materials, simultaneously extracting REEs present within the PMs and transferring them into the slag phase. Thermodynamic modeling, via Factsage Equilib stream calculations, optimized the experimental process. Simulated materials substituted the PM motor, which optimized modeling-directed melting within an induction furnace. The 2FeO·SiO₂ fayalite flux can oxidize rare earth elements, resulting in slag. The neodymium oxidation reaction by fayalite exhibits a ΔG° of −427 kJ when subjected to an oxygen partial pressure ($P_{O_2}$) of 1.8 × 10⁻⁹, which is lower than that required for FeO decomposition. Concerning the FeO–SiO₂ system, neodymium, in Nd³⁺, exhibits a strong bonding with the ${\mathrm{S}\mathrm{i}\mathrm{O}}_4^{4^{-}}$ matrix, leading to its incorporation within the slag as the silicate compound, Nd₂Si₂O₇. When 30 wt.% fayalite flux was added, the resulting experiment yielded a neodymium extraction degree of 91%, showcasing the effectiveness of this fluxing agent in the extraction process. Full article

Fault detection is a critical research area, especially in the automotive sector, aiming to quickly assess component conditions. Machine Learning techniques, powered by Artificial Intelligence, now represent state-of-the-art methods for this purpose. This study focuses on durability testing of Permanent Magnet Synchronous Motors for automotive applications, using Autoencoders (AEs) to predict and prevent failures. This AI-based fault detection strategy employs acceleration signals coming from electric motors tested under challenging conditions with significant variations in torque and speed. This approach goes beyond typical fault detection in steady-state conditions. Based on a review of Neural Networks, including Variational Autoencoders (VAEs), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the performance of six AI architectures is compared: AE, VAE, 1D CNN AE, 1D CNN VAE, LSTM AE and LSTM VAE. The 1D CNN AE outperformed the other networks in fault detection, showing high accuracy, stability and computational efficiency. The model is integrated into an algorithm for semi-real-time fault monitoring. The algorithm effectively detects potential motor failures in real-world scenarios, including bearing faults, mechanical misalignments, and progressive wear of components, thereby proactively preventing damage and halving test bench downtime. Full article

In recent years, as an important alternative to traditional gasoline-powered vehicles, new electric vehicles (NEVs) have gained widespread attention and rapid development globally. In the traditional automotive industry chain, downstream vehicle manufacturers need to master core technologies, such as engines, chassis, and transmissions. In contrast to the traditional automotive industry chain, where downstream vehicle manufacturers must master core technologies, like engines, chassis, and transmissions, the electric vehicle industry chain has evolved in a way that the development of core components is gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now outsource key components, such as batteries, electric controls, and motors. Additionally, in terms of sales models, the electric vehicle industry chain can adopt either the traditional 4S dealership model or a direct-sales model. As the research and development of core components are increasingly separated from vehicle manufacturers, the downstream vehicle manufacturers can source components, like batteries, electric controls, and motors, externally. At the same time, they can choose to use either the traditional 4S dealership model or the direct-sales model. The underlying mechanisms and channel selection in this context require further exploration. Based on this, a mathematical model is established by incorporating terminal marketing input, product competitiveness, and after-sales service levels from the literature to solve for the optimal pricing under centralized and decentralized pricing strategies. Using numerical examples, the pricing and profit performance under different market structures are analyzed to systematically examine the impact of the electric vehicle supply chain on business operations, as well as the changes in various elements across different channels. We will focus on how after-sales services (including the spare part supply) influence the pricing strategy and profit distribution in the supply chain, aiming to provide insights into advanced manufacturing system management for manufacturing enterprises and improve the efficiency of intelligent logistics management. The research indicates that (1) The direct-sales model helps to improve the terminal marketing input, after-sales service quality, and product competitiveness for supply chain stakeholders; (2) It is noteworthy that the manufacturer’s direct-sales model also significantly contributes to lowering prices, highlighting that the direct-sales model has substantial impacts on both supply chain stakeholders and, importantly, consumers. Full article

The automotive industry is one of the most automatized industries, employing more than one million robots worldwide. Although several steps in car production are completely automated, many steps are still carried out by operators, especially in tasks requiring high dexterity. Additionally, customization and deployability are still pending issues in this industry, where a real collaboration between robots and operators would increase the reconfigurability of the assembly lines. This paper presents an innovative robotic cell focused on the motor and gearbox assembly, including collaborative industrial robots and autonomous mobile manipulators along the different assembly stations. The design also incorporates a human-centered approach, with an enhanced human interface to facilitate the interaction with operators with the complete robotic cell. The proposed approach has been deployed and validated on a real automotive industrial scenario, obtaining promising metrics and results. Full article

The automotive industry faces increasing challenges due to fuel scarcity and pollutant emissions, necessitating the implementation of strategies that optimize engine performance while minimizing the environmental impact. This study aimed to analyze the influence of oil viscosity and fuel quality on the engine performance and pollutant emissions in an internal combustion engine. A Response Surface Methodology (RSM)-based experimental design was employed. Three oil viscosity levels (SAE 5W-30, 10W-30, and 20W-50) and three fuel quality levels (87, 92, and 95 octane) were evaluated using a Chevrolet Grand Vitara 2.0L (General Motors, Quito, Ecuador) tested on a dynamometer. The oil grades were selected to represent a practical range of viscosities commonly used in commercial vehicles operating under local conditions. The results indicate that using lower-viscosity oil (SAE 5W-30) increased the engine power by up to 6.25% compared to when using SAE 20W-50. Additionally, using higher-octane fuel led to an average power increase of 1.49%, attributed to improved combustion stability and the ability to operate at a more advanced ignition timing without knocking. The emissions analysis revealed that high-viscosity oil at high RPMs increased CO₂ emissions to 14.4% vol, whereas low-viscosity oil at low RPMs reduced CO₂ emissions to 13.4% vol. Statistical analysis confirmed that the engine speed (RPM) was the most influential factor in emissions (F = 163.11 and p < 0.0001 for CO₂; F = 247.02 and p < 0.0001 for NOx), while fuel quality also played a significant role. These findings suggest that optimizing the oil viscosity and selecting the appropriate fuel can enhance engine efficiency and reduce emissions, thereby contributing to the development of more sustainable automotive technologies. Future research should explore the use of ultra-low-viscosity lubricants (SAE 0W-20) and assess their long-term effects on engine wear. Full article

Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of a Permanent Magnet Synchronous Motor (PMSM). While proportional–integral (PI) controllers remain a widely adopted choice for FOC due to their simplicity, their performance can degrade significantly under high-frequency speed transitions, where nonlinear dynamics introduce notable inaccuracies. The TinyFC model complements the PI controller by learning the intrinsic dependencies within the control loops and generating corrective signals to alleviate these inaccuracies. To ensure practical implementation, TinyFC underwent extensive optimization procedures, incorporating advanced techniques such as hyperparameter tuning, pruning, and 8-bit quantization. These measures successfully reduced the model’s computational overhead while preserving predictive accuracy. Simulation results demonstrated that embedding TinyFC within the FOC framework substantially reduced overshoot, with the pruned TinyFC entirely eliminating overshoot when integrated into the speed control unit. These findings highlight the feasibility of employing lightweight neural networks for real-time motor control applications, establishing a foundation for more efficient and precise control strategies in edge automotive and industrial systems. Full article

This paper presents a comprehensive methodology to enable the optimization of an automotive electric axle to reduce noise emissions and improve load distribution. The proposed method consists of the application of two sequential optimization procedures. The first one focuses on the gears’ macro-geometry, based on an objective function that combines the contact ratio, power loss, and center distance. The second one optimizes the micro-geometry of the teeth to reduce the sound pressure generated by tooth impacts. Mechanical stress limits are considered as a constraint in the optimization process. Shafts, joints, and the electric motor are analyzed, taking into account their deformation that influences the dynamics of the entire system. The results of the proposed procedure are verified through experimental measurements and the comparison can be considered successful. Full article

Motor vehicles emit a large amount of air pollutants. Inspection and Maintenance (I/M) systems serve as a pivotal strategy for mitigating emissions from operational diesel trucks. However, the prevalent issue of blind repairs persists due to insufficient diagnostic capabilities at maintenance stations (M stations). To address this challenge, a multi-source information fusion methodology is proposed, integrating load deceleration testing from inspection stations (I stations), on-board diagnostics (OBD) data, and manual measurements at M stations. Critical diagnostic parameters—including nitrogen oxides (NOx) and particulate matter (PM) emissions, the ratio of measured wheel-side power to rated power, intake volume, common rail pressure, and exhaust back pressure—are systematically selected through statistical analysis and expert evaluations. An adaptive membership function is developed to resolve ambiguities in emission thresholds, enabling the construction of a robust fault diagnosis framework. Validation using 800 National V diesel truck maintenance records from a provincial automotive electronic health platform (2022 data) demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%. By minimizing unnecessary repairs and optimizing maintenance efficiency, this approach significantly reduces resource waste and the lifecycle environmental footprints of diesel fleets. The proposed fuzzy-logic-based model effectively detects latent faults during routine maintenance, directly contributing to sustainable transportation through reductions in NOx and PM emissions—critical for improving air quality and advancing global climate objectives. This establishes a scalable technical framework for the effective implementation of I/M systems in alignment with sustainable urban mobility policies. Full article

Conventional active vehicle height control systems predominantly employ hydraulic or pneumatic suspension mechanisms. Although these established approaches have achieved widespread adoption in automotive applications, they remain fundamentally constrained by three critical drawbacks: (1) inadequate dynamic response characteristics, (2) high energy consumption, and (3) inherent mechanical complexity. The ongoing electrification revolution in vehicle technologies has spurred significant research interest in linear electromagnetic suspension systems. Nevertheless, their practical implementation encounters dual technical barriers: (a) complex multi-phase motor configurations requiring precise coordination, and (b) substantial thrust ripple generation under dynamic operating conditions. To address these critical limitations, our research proposes a novel motor structure, known as the flat rectangular slot structure, which offers advantages such as simple installation and high thrust with low current. Additionally, we have designed a double-vector control strategy for the motor control section, which modifies the finite-set model predictive control and enhances the accuracy of the model’s calculations. By integrating the vehicle model, we have developed a multi-layer hierarchical control strategy for the vehicle height controller. In the first layer, a PI controller is used to convert the target height into current, which is then input into the value function. In the second layer, we improve the control strategy for the linear motor by optimizing the finite-set model predictive control through the double-vector control. Through multi-step predictive calculations, we determine the optimal sector, enabling the motor to receive the corresponding control force. In the third layer, the motor thrust is input into the vehicle model to achieve closed-loop control of the vehicle body. Finally, we conduct simulation verification of the proposed control strategy. The simulation results indicate that the double-vector control significantly reduces the fluctuation in the sprung mass displacement by approximately 70% compared to single-vector control, the response speed is increased by approximately 20%, and the thrust required to achieve the target vehicle height is reduced by 5.7%. Therefore, the proposed double-vector control strategy can significantly enhance the stability of the automotive electronic control suspension, opening up new research avenues for the study of suspension stability control and energy saving in vehicles. Full article