This study develops and validates a practical approach to estimating and reducing fuel consumption in urban bus operations using real-world data from Transinco B80 buses on Route 30 (Ho Chi Minh City). Against the company supply baseline ( Q c c = 24.42 L/66 km), the computed actual consumption under current practice is Q ct = 22 . 41 L (8.23% lower). Two gear-skipping strategy are assessed: (i) skip first, Q bp 1 = 22 . 37 L (8.39%, marginal +0.2 pp vs current practice) and (ii) skip second, Q bp 2 = 21 . 71 L (11.14%, +2.9 pp). Uncertainty propagation yields ±0.55 L/66 km (≈ ±2.26 pp vs Q cc ). Hence, measure (ii) remains robustly beneficial, whereas measure (i) is not practically distinguishable from noise. A simple longitudinal simulation reproduces smoother acceleration/jerk under gear-skipping and is consistent with the observed reductions. At fleet scale, the measures translate to meaningful monthly savings. These results support low-cost, behavior-centric strategies to improve fuel efficiency in urban bus systems.

Failures associated with braking systems and insufficient following distances are primary causes of rear-end collisions and rollovers involving trucks. Through a comprehensive analysis of hazardous states during braking and their contributory factors, a vehicle en-route status detection device has been developed. This device can acquire real-time data on load, vehicle speed, and the state of the braking system. Hazard identification based on single parameters such as brake shoe wear, abnormal brake shoe temperatures, and brake light failures has been conducted using sensor data. By performing a dynamic analysis of vehicles during braking, a braking distance calculation model was established. This model was calibrated through vehicle coasting tests. The reliability of the braking distance model was validated through both Automatic Dynamic Analysis of Mechanical Systems (ADAMS) simulation and practical vehicle tests. Considering the relationships among the following distances, brake line pressures, and braking distances, a novel multi-parameter identification method for hazardous states during the braking process is proposed.

High fuel consumption in heavy-duty trucks contributes significantly to operating costs and carbon dioxide (CO 2 ) emissions. While predictive cruise control (PCC) is an effective technology for optimizing vehicle speed, its energy-saving potential can be further enhanced. This paper proposes a fused control strategy that synergizes the PCC system with an intelligent, multi-mode coasting function. The algorithm leverages the PCC-generated speed trajectory to make optimal decisions between in-gear and neutral coasting, thereby maximizing the use of vehicle inertia. Simulations, benchmarked against a conventional PCC system, demonstrate that the proposed strategy achieves an additional energy saving of up to 5.73%, with a corresponding reduction in CO 2 emissions.

Giant magnetostrictive actuators (GMAs) usually have the problem of significant temperature change caused by power losses under pulse width modulation (PWM) excitation. It leads to thermal deformation and a decrease in the magnetostriction coefficient, which degrades their performance of position control. This paper introduces a novel dual-flow-channel (DFC) cooling structure that includes outer and inner flow channels. Flow grooves are machined into the magnetic sleeve to form the outer flow channel in conjunction with the shell, while the inner flow channel immerses the giant magnetostrictive material (GMM) rod. DFC cooling structure achieves efficient heat dissipation for GMA while preserving its original volume and maintaining good sealing. A thermal analytical model of GMA, based on the lumped-parameter thermal network (LPTN), is employed to achieve rapid and accurate prediction of thermal characteristics. Based on the Jordan loss model, the GMM loss model under sinusoidal excitation was improved to develop a GMM loss model under PWM excitation. A prototype of GMA was designed and machined, and its thermal characteristics were tested. The experimental results show that, under ambient conditions of 20 °C, with a current amplitude of 5 A, a PWM frequency of 50 Hz, and a coolant flow rate of 0.5 L/min, the final temperature of the GMA with the DFC cooling structure decreased from 45 °C to 23.5 °CC. The relative errors between the analytical and experimental results were <11% under different conditions, validating the accuracy of the proposed analytical model. This study provides important theoretical support for the investigation of thermal deformation in GMAs under PWM excitation.

To extend the service life of garbage truck bucket arms, this paper proposes a redesign framework that prioritizes structural integrity while achieving lightweight performance. A parametric finite element model of the bucket arm has been developed to minimize mass, deformation, and von Mises stress. Design parameters are systematically investigated through a Box–Behnken design (BBD), and a radial basis function (RBF) surrogate model is constructed to approximate the objective functions. The accuracy of the RBF model is validated by comparison with Kriging and response surface methodology (RSM). Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed for multi-objective optimization, resulting in 333 lightweight design schemes that meet all structural requirements. The optimal solution is then identified using the CRITIC–GRA method. Results show that the redesigned bucket arm achieves a maximum stress reduction of 37.4% and a stiffness enhancement of 61.8% relative to the initial design, accompanied by a 16.6% increase in mass.

This manuscript presents an integrated approach combining chemical kinetics modeling with computational fluid dynamics (CFD) simulation to investigate oil degradation during the operation of a maritime diesel engine MTU 10V 2000 M72. Using COMSOL Multiphysics 6.2, a detailed 2D simulation was performed to model key engine components, including the main journal bearings, connecting rod journal bearings, piston, and oil pan. Three key parameters were analyzed: heat of combustion, crankshaft rotational speed, and oil flow rate. Results demonstrate that while heat of combustion has a moderate impact on oil lifespan, rotational speed significantly affects degradation due to its influence on temperature and flow conditions. The oil flow rate resulted as the most critical factor, with higher flow rates effectively reducing oil temperature and extending its lifespan by minimizing exposure to elevated temperatures. This study highlights the complex interplay between heat generation, flow dynamics, and chemical degradation, providing insights for improved lubricant performance, predictive maintenance strategies, and enhanced engine reliability.

This study presents a data-driven approach for smart tire management through the application of unsupervised machine learning techniques. Using a real-world dataset comprised with synchronized records collected at 1 Hz from a sensor-equipped fleet vehicle, the research investigates how the use of clustering algorithms – K-Means and BIRCH with Agglomerative Clustering – can be employed to identify distinct operational stages in the usage cycle of tires. A comprehensive descriptive analysis was first conducted to understand the behavior and correlations among pressure, temperature, and speed data. The clustering analysis, applied both globally and by individual tire positions, revealed that the optimal number of clusters can vary depending on the tire’s location. The findings highlight the importance of position-aware tire analytics and support the development of intelligent tire management systems capable of optimizing performance, enhancing safety, and extending tire lifespan.

This paper establishes a thermal analysis model for parametric profile modification gears based on the heat transfer mechanisms of gear transmission. The main research parameters are the addendum coefficient and the modification coefficient, focusing on the spatial distribution of frictional heat flux and the gear body temperature field. The study analyzes the effects of two different methods of parametric profile modification on thermal characteristics. The results indicate that reducing the addendum coefficient can improve thermal characteristics, effectively lowering both the amplitude and gradient of the tooth surface temperature. Conversely, a decrease in the modification coefficient increases the tooth surface temperature and leads to a more uneven spatial distribution of the temperature field in the gear body, thereby deteriorating its thermal characteristics. A sensitive interval for the influence of tooth profile modification parameters on thermal characteristics is proposed, establishing an intuitive connection between tooth profile modification parameters and the thermal characteristics of gears, which provides theoretical support for the parametric design method of thermal tooth profile modification.

The static/dynamic stiffness of the ball screw feed system has an important influence on the machining accuracy and stability of the machine tool. To study the static/dynamic stiffness characteristics of the ball screw feed system under different support methods, a dynamic model of the ball screw feed system is established using the lumped parameter method. Then, the variation of the system’s static/dynamic stiffness is studied, and the influence of workpiece mass and placement position on the system’s static/dynamic stiffness is also concerned. The results show that as the stroke increases, the static/dynamic stiffness of the system with fixed supports at both ends first decreases and then increases, while that of the system with one end fixed and the other end floating always decreases. Comparing the two systems, the dynamic stiffness of the one with fixed supports at both ends is more sensitive to the workpiece’s mass and placement position. At the same time, as the workpiece mass increases, the natural frequency of the ball screw feed system decreases. The closer the workpiece is to the center of gravity of the worktable, the smaller its influence on the system’s natural frequency.

High-precision surface finishing in manufacturing relies heavily on the condition of the grinding wheel, where any imbalance, cracks, or wear can degrade quality. This research aims to develop an AI-driven health monitoring framework by integrating experimentally acquired vibration data with a validated mathematical model, enabling accurate detection of grinding wheel conditions in real time. The study introduces a dual-framework approach combining statistical feature extraction from vibration signals with a physics-based model rooted in Newtonian mechanics and Fourier analysis. This integration enhances both the interpretability and robustness of fault classification, providing a novel, data-validated solution for predictive maintenance. Vibration signals were recorded for four wheel states—good, unbalanced, cracked, and worn—under controlled operational conditions. Ten statistically significant features, ranked using a Random Tree algorithm, were classified using a Random Forest model. Parallelly, a dynamic response model of the grinding wheel was developed and validated against experimental results. The Random Forest classifier achieved a 98.5% overall accuracy (Kappa = 0.98) using the top 10 features, with minimal misclassification (1.5%, primarily between cracked and unbalanced wheels). Mean vibration amplitudes ranged from 0.014 g (good condition) to 0.078 g (unbalanced), with one-way ANOVA confirming significant differences ( p < 0.001). The mathematical model reproduced experimental accuracy trends, with classification performance stabilizing at 98.5% when ≥9 features were used, and maintained 96.8% accuracy under ±5% Gaussian noise. The proposed integrated approach enables high-fidelity, real-time condition monitoring of grinding wheels, reducing downtime and enhancing reliability. By coupling experimental validation with a physically grounded model and quantitative AI-based classification, this work surpasses conventional black-box approaches, offering an interpretable and industrially scalable predictive maintenance solution.