Search Results (363)

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

This study proposes an enhanced integration framework for the global navigation satellite system (GNSS) and inertial navigation system (INS). The framework combines real-time differential GNSS corrections with an adaptive extended Kalman filter (EKF) to address positional accuracy and system robustness challenges in practical navigation scenarios. The proposed method dynamically compensates for positioning inaccuracies and sensor drift by integrating differential GNSS corrections to reduce errors and employing an adaptive EKF to address temporal synchronization discrepancies and misalignment angle deviations. Simulation and experimental results demonstrate that the framework keeps horizontal positioning error within 2 m and achieves a maximum accuracy improvement of 4.2 m compared to conventional single-point positioning. This low-cost solution ensures robust performance for practical autonomous navigation scenarios. Full article

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

In this study, the sensitivity of operating parameters such as the hydrogen and air excess coefficient, cathode inlet pressure, intake relative humidity, and coolant inlet temperature and their effects on the performance of single proton exchange membrane fuel cells (PEMFCs) are experimentally assessed. The results revealed that the fuel cell node voltage increases as the hydrogen and air excess coefficient increases, and the impact of the hydrogen and air excess coefficient on the fuel cell node voltage gradually increases as the current density increases. However, a higher hydrogen and air excess coefficient is not always better. The node voltage increases as the intake pressure increases. However, it is not that a higher intake pressure is always better, but rather that there is an optimal intake pressure value to achieve the best overall performance of the fuel cell. The node voltage increases as the coolant inlet temperature increases at most fuel cell current densities. However, the optimum fuel cell operating inlet temperature is not necessarily higher, as the coolant inlet temperature may have a strong coupling relationship with other operating conditions that will also affect the fuel cell performance. The fuel cell operating inlet temperature may have a strong coupling relationship with the intake relative humidity, and both of these parameters must be well-matched to achieve better fuel cell performance. Full article

End milling is a process that is widely used for producing components in aerospace applications, automobile applications, and many other fields. It is crucial to forecast a workpiece’s deformation behaviour during the machining process to choose the best process settings and maximize the part’s overall quality. Understanding the behaviour of each workpiece during the end-milling process through physical experiments is critical, but expensive. Hence, it is inevitable that a numerical study will be developed to estimate workpiece deformation with higher accuracy and less computational cost. The end-milling process on AISI 1045 is simulated in this work using a 3D finite element modelling technique. The ANSYS Workbench 2020 R1 is used to conduct an explicit dynamic analysis in the suggested model. The workpiece’s stress and deformation values throughout the machining process are estimated and examined. Full article

The power system, which is also one of the most crucial parts of fuel cell cars, marks the biggest distinction between them and conventional automobiles. Fuel cell hybrid power systems are reviewed in this paper along with their current state of research. Three different kinds of fuel cell hybrid power systems—fuel cell–battery, fuel cell–supercapacitor, and fuel cell–battery–supercapacitor—are thoroughly compared and analyzed, and they are systematically explained in the three areas of passenger cars, buses, and heavy duty trucks. Existing fuel cell hybrid systems and energy strategies are systematically reviewed and summarized, including predictive control strategies based on game theory, power allocation strategies, fuzzy control strategies, and adaptive super twisted sliding mode control (ASTSMC) energy management techniques. This study offers recommendations and direction for the future direction of fuel cell hybrid power system research and development. Full article

Bicycle use has been increasing in many countries for active, sustainable transportation and recreation. Bicycling can benefit an individual’s mental and physical health and contribute to a community’s well-being and desirability, and it is more environmentally sustainable than automobiles. Nonprofit organizations lead bicycle advocacy efforts in the USA, both for bicycling as recreation and as part of local transportation systems. Outride is one of the larger advocacy organizations, and it sponsors a unique grant system targeting grassroots bicycling organizations dedicated to increasing bicycling. Using the Bicycle Community Development Framework (BCDF) as a lens, this study aims to evaluate Outride’s efforts through an interpretative phenomenological approach (IPA) using semi-structured interviews to gather data regarding grant recipients’ experiences using Outride funds. Findings suggest fund recipients are increasing bicycling through programs and infrastructure development, but with more intentionality, could better support building bicycle communities. Regarding the BCDF, the recipients strongly promoted education, engineering, and equity & accessibility while fostering a sense of community, belonging, and empowerment in their participants. Full article

The high-fidelity lithium-ion battery (LIB) models are crucial for realizing an accurate state estimation in battery management systems (BMSs). Recently, the fractional-order equivalent circuit models (FOMs), as a frequency-domain modeling approach, offer distinct advantages for constructing high-precision battery models in field of electric vehicles. However, the quantitative evaluations and adaptability of these models under different driving cycle datasets are still lacking and challenging. For this reason, comparative evaluations of different FOMs using a novel drive cycle dataset of a battery was carried out in this paper. First, three typical FOMs were initially established and the particle swarm optimization algorithm was then employed to identify model parameters. Complementarily, the efficiency and accuracy of the offline identification for three typical FOMs are also discussed. Subsequently, the terminal voltages of these different FOMs were investigated and evaluated under dynamic operating conditions. Results demonstrate that the FOM-W model exhibits the highest superiority in simulation accuracy, achieving a mean absolute error (MAE) of 9.2 mV and root mean square error (RMSE) of 19.1 mV under Highway Fuel Economy Test conditions. Finally, the accuracy verification of the FOM-W model under two other different dynamic operating conditions has also been thoroughly investigated, and it could still maintain a RMSE and MAE below 21 mV, which indicates its strong adaptability and generalization compared with other FOMs. Conclusions drawn from this paper can further guide the selection of battery models to achieve reliable state estimations of BMS. Full article

The challenges of global warming and other environmental concerns have prompted governments worldwide to transition from fossil-fuel vehicles to low-emission electric vehicles (EVs). The energy crisis, coupled with environmental issues like air pollution and climate change, has been a driving force behind the development of EVs. In recent years, EVs have emerged as one of the most innovative and vital advancements in clean transportation. According to recent reports, EVs are gradually replacing traditional automobiles, offering benefits such as pollution reduction and the conservation of natural resources. This research focuses on analyzing and reviewing the impact of EV integration on electrical networks, with particular attention to photovoltaic (PV) energy as a sustainable charging solution. It examines both current and anticipated challenges, especially those related to power quality, harmonics, and voltage imbalance. A special emphasis is placed on Tunisia, a country with high solar energy potential and increasing interest in EV deployment. By exploring the technical and infrastructural readiness of Tunisia for PV-based EV charging systems, this paper aims to inform regional strategies and contribute to the broader goal of sustainable energy integration in developing countries as part of future work. Full article

Fins are extended surfaces designed to increase heat dissipation from hot sources to their surroundings. Heat transfer is improved by utilising fins of different geometrical shapes. Fins are extensively used in automobile parts, solar panels, electrical equipment, computer CPUs, refrigeration systems, and superheaters. Motivated by these applications, this study investigates the incorporation of magnetic fields and porosity into a convective–radiative triangular fin to enhance heat transfer performance. The shooting technique is applied to study thermal profile and efficiency of the fin. It is found that the magnetic number (Hartmann number), porosity, convective, and radiative parameters reduce the thermal profile, while the Peclet number and ambient temperature increase it. Moreover, the efficiency increases with an increase in the magnetic number, porosity, convective, and radiative parameters, whereas it declines with an increase in the Peclet number and ambient temperature. Increasing the magnetic number from $0.1$ to $0.7$ leads to a $4\%$ reduction in the temperature profile. Similarly, raising the porosity parameter within the same range results in an approximate $3\%$ decrease in the thermal profile. An increase in the convective parameter from $0.1$ to $0.7$ causes about an $8\%$ decline in the thermal profile, while an elevation in the radiative parameter within the same range reduces it by approximately $2\%$. In contrast, enhancing the Peclet number from $0.1$ to $0.7$ increases the thermal profile by nearly $2\%$, and a rise in the ambient temperature within this range leads to an approximate $4\%$ enhancement in the thermal profile. Magnetised triangular fins are observed to have higher thermal transfer ability and efficiency than non-magnetised triangular fins. It is found that the incorporation of a magnetic field into a triangular fin, in conjunction with the porosity, improves the performance and efficiency of the triangular fin. Full article

Poly(acrylonitrile-butadiene-styrene) (ABS) is a common polymer used in toys, automobile parts, and membranes. Membranes fabricated with this copolymer commonly employ toxic solvents and have a dense architecture, which may not work in all applications. This work investigates the synthesis of ABS membranes, using green solvents and the influence of additives on the phase inversion process during the non-solvent-induced phase separation. The addition of water-soluble additives, ethanol, and acetone is hypothesized to provide additional control over viscosity and volatility, and, consequently, impact the phase inversion process. Membranes were fabricated with PolarClean and with various additive concentrations and evaporation times. The resulting membranes were characterized using scanning electron microscopy (SEM) and a pycnometer to visualize the pore structure and obtain porosity information. Membrane performance, including water flux and bovine serum albumin rejection, was evaluated using dead-end cell filtration. Membranes fabricated using only PolarClean had fingerlike pore morphology and relatively low protein rejection. The addition of additives resulted in a change in pore architecture and rejection, which is hypothesized to be a result of additives’ volatility, humidity, and destabilization of liquid–liquid separation. This study provides a more detailed understanding of the impact of additives on the resulting ABS membrane structure and performance, with a focus on safer solvents. Full article

Additive manufacturing has reached a level of reliability and credibility that has already been integrated into specific industries producing final parts or tooling. Among Material Extrusion (ME) techniques, the Fused Granular Fabrication (FGF) method has enabled the development of Large Format Additive Manufacturing (LFAM) using polymeric materials, which has also established its presence in industries working with large prototypes, molds, and tools. This cost-efficient process has proven its applicability and success in manufacturing molds for composites, particularly in short and medium production runs, significantly reducing production times and costs. This paper presents two experiments designed to optimize process parameters when producing molds using the combined FGF and milling approach. These experiments identified optimal extrusion temperatures and extrusion multipliers to minimize defects at both the macro- and microscales for ASA 20 wt.% carbon fiber (CF) material; additionally, a correlation between milling speed, milling strategy, and surface roughness was established. These findings are valuable for industries adopting this innovative production method, as they provide guidance for defining process parameters to achieve the desired surface roughness of a specific part. A case study of the design of an automobile carter mold is presented, concluding that a specific range of milling speeds is required for conventional or climbing milling strategies to achieve a defined surface roughness range. Full article

The automotive brake piston component is an important part of the automotive brake system, and the concentricity detection of the first piston component is crucial to ensure driving safety. In this paper, an improved Canny algorithm is proposed for non-contact detection of spring concentricity of the first piston component. Firstly, the traditional Canny algorithm is improved by replacing the Gaussian filter with a bilateral filter to fully retain the edge information, and accurate edge detection results are obtained by constructing a multi-scale analysis. After obtaining the edge images, a sub-pixel edge detection method with gray moments is introduced to optimize these edges; secondly, a circle is fitted to the extracted edge points by using the RANSAC algorithm to determine the center position and radius of the circle; and finally, the concentricity of the first piston part is calculated based on the fitting results. The experimental results are compared with those of the CMM and the traditional Canny algorithm, and the results show that the improved Canny algorithm reduces the coaxiality error by 4% and enables effective measurement of the concentricity of the first piston assembly spring. Full article