Search Results (401)

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

This paper details the design and implementation of a fuzzy logic control system for an advanced driver-assistance system (ADAS) that adjusts brake force based on proximity sensing, vehicle speed, and road conditions. By employing a cost-effective ultrasonic sensor (HC-SR04) and an STM32 microcontroller, the system facilitates real-time adjustments to braking force, enhancing both vehicle safety and driver comfort. The fuzzy logic controller processes three inputs to deliver a smooth and adaptive brake response, thus addressing the shortcomings of traditional binary systems that can lead to abrupt and unsafe braking actions. The effectiveness of the system is validated through several test cases, demonstrating improved responsiveness and safety across various driving scenarios. This paper presents a cost-effective model for a straightforward braking system using fuzzy logic, laying the groundwork for the development of more advanced systems in emerging technologies. Full article

Dual-motor electric vehicles enhance power performance and overall output capabilities by enabling the real-time control of the torque distribution between the front and rear wheels, thereby improving handling, stability, and safety. In addition to increased energy efficiency, a dual-motor system provides redundancy: if one motor fails, the other can still supply partial power, further enhancing driving safety. This study aimed to optimize the energy management strategies of the front- and rear-axis motors, examining the application effects of rule-based control (RBC), global grid search (GGS), and the whale optimization algorithm (WOA). A simulation platform based on MATLAB/Simulink^® (R2021b, MATLAB, Natick, MA, USA) was constructed and validated through hardware-in-the-loop (HIL) testing to ensure the authenticity and reliability of the simulation results. Detailed tests and analyses of the dual-motor system were conducted under FTP-75 driving cycles. Compared to the RBC strategy, GGS and WOA achieved energy efficiency improvements of 9.1% and 8.9%, respectively, in the pure simulation, and 4.2% and 3.8%, respectively, in the HIL simulation. Compared to the pure RBC strategy, the RBC and GGS strategies incorporating regenerative braking achieved energy efficiency improvements of 26.1% and 29.4%, respectively, in the HIL simulation. Overall, GGS and WOA each present distinct advantages, with WOA emerging as a highly promising alternative energy management strategy. Future research should further explore WOA applications to enhance energy savings in real-world vehicle operations. Full article

There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article

The integration of biomimetic pectoral fins into robotic fish presents a promising approach to enhancing maneuverability, stability, and braking efficiency in underwater robotics. This study investigates a 1-DOF (degree of freedom) pectoral fin mechanism integrated into the SpineWave robotic fish. Through force measurements and particle image velocimetry (PIV), we optimized control parameters to improve braking and turning performances. The results show a 50% reduction in stopping distance, significantly enhancing agility and control. The fin-assisted braking and turning modes enable precise movements, making this approach valuable for autonomous underwater vehicles. This research lays the groundwork for adaptive fin designs and real-time control strategies, with applications in underwater exploration, environmental monitoring, and search-and-rescue operations. Full article

Distributed-drive electric vehicles (DDEVs), through independent, rapid, and precise control of the driving/braking torque of each wheel, offer unprecedented opportunities to enhance their handling stability, ride comfort, energy economy, and safety. However, their inherent over-actuation characteristics and multi-degree-of-freedom motion coupling pose significant challenges to the vehicle chassis control system. Chassis coordinated control, by coordinating multiple subsystems such as drive, braking, steering, and suspension, has become a key technology to fully leverage the advantages of distributed drive and address its challenges. This paper reviews the core issues in chassis coordinated control for DDEVs, comparatively analyzes several distributed electric drive coordinated control architectures, and systematically outlines recent research progress in lateral–longitudinal, lateral–vertical, longitudinal–vertical, and combined three-dimensional (lateral–longitudinal–vertical) coordinated control, including control architectures, key technologies, commonly used algorithms, and control allocation strategies. By analyzing and comparing the advantages, disadvantages, and application scenarios of different coordinated control schemes, this paper summarizes the key scientific problems and technical bottlenecks in this field and looks forward to development trends in intelligence, integration, and scenario-based fusion, aiming to provide a reference for the development of high-performance chassis control technology for DDEVs. Full article

Adaptive vehicle control systems are crucial for enhancing safety, performance, and efficiency in modern transportation, particularly as vehicles become increasingly automated and responsive to dynamic environments. This review explores the advancements in bio-inspired actuators and their potential applications in adaptive vehicle control systems. Bio-inspired actuators, which mimic natural mechanisms such as muscle movement and plant tropism, offer unique advantages, including flexibility, adaptability, and energy efficiency. This paper categorizes these actuators based on their mechanisms, focusing on shape memory alloys, dielectric elastomers, ionic polymer–metal composites, polyvinylidene fluoride-based electrostrictive actuators, and soft pneumatic actuators. The review highlights the properties, operating principles, and potential applications for each mechanism in automotive systems. Additionally, it investigates the current uses of these actuators in adaptive suspension, active steering, braking systems, and human–machine interfaces for autonomous vehicles. The review further outlines the advantages of bio-inspired actuators, including their energy efficiency and adaptability to road conditions, while addressing key challenges like material limitations, response times, and integration with existing automotive control systems. Finally, this paper discusses future directions, including the integration of bio-inspired actuators with machine learning and advancements in material science, to enable more efficient and responsive adaptive vehicle control systems. Full article

Predictive maintenance in commercial aviation demands highly reliable and robust models, particularly for critical components like carbon brakes. This paper addresses two primary concerns in modeling carbon brake wear for distinct aircraft variants: (1) the choice between developing specialized models for individual aircraft types versus a unified, general model, and (2) the potential of transfer learning (TL) to boost model performance across diverse domains (e.g., aircraft types). We evaluate the trade-offs between predictive performance and computational efficiency by comparing specialized models tailored to specific aircraft types with a generalized model designed to predict continuous wear values across multiple aircraft types. Additionally, we explore the efficacy of TL in leveraging existing domain knowledge to enhance predictions in new, related contexts. Our findings demonstrate that a well-tuned generalized model supported by TL offers a viable approach to reducing model complexity and computational demands while maintaining robust and reliable predictive performance. The implications of this research extend beyond aviation, suggesting broader applications in component predictive maintenance where data-driven insights are crucial for operational efficiency and safety. Full article

In the field of active safety technology for intelligent connected vehicles (ICVs), the reliability and safety of the Automatic Emergency Braking (AEB) system is recognized as critical to driving safety. However, existing evaluation methods have been constrained by the inadequacy of static weight assessments in adapting to diverse driving conditions, as well as by the disconnect between conventional evaluation frameworks and experimental validation. To address these limitations, a comprehensive Vehicle-in-the-Loop (VIL) evaluation system based on the dynamic weight analytic hierarchy process (DWAHP) was proposed in this study. A two-tier dynamic weighting architecture was established. At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. At the scheme level, a five-dimensional indicator system—integrating braking distance, collision speed, and other key metrics—was constructed to support an adaptive evaluation model under multi-condition scenarios. By establishing a dynamic mapping between weight functions and driving condition parameters, the DWAHP methodology effectively overcame the limitations associated with fixed-weight mechanisms in varying operating conditions. Based on this framework, a dedicated AEB system performance test platform was designed and developed. Validation was conducted using both VIL simulations and real-world road tests, with a Volvo S90L as the test vehicle. The experimental results demonstrated high consistency between VIL and real-world road evaluations across three dimensions: safety (deviation: 0.1833/9.5%), reliability (deviation: 0.2478/13.1%), and riding comfort (deviation: 0.05/2.7%), with an overall comprehensive score deviation of 0.0707 (relative deviation: 0.51%). This study not only verified the technical advantages of the dynamic weight model in adapting to complex driving environments and analyzing multi-parameter coupling effects but also established a systematic methodological framework for evaluating AEB system performance via VIL. The findings provide a robust foundation for the testing and assessment of AEB system, offer a structured approach to advancing the performance evaluation of advanced driver assistance systems (ADASs), facilitate the safe and reliable validation of ICVs’ commercial applications, and ultimately contribute to enhancing road traffic safety. Full article

As a key component of the train braking system, the comprehensive performance of brake discs plays a vital role in ensuring the operational safety of trains. With the advent of high-speed and heavy-haul trains, the thermal energy generated by braking systems has significantly increased. The resulting rapid temperature rise can easily exceed the material limits of brake discs. Consequently, research focused on enhancing brake disc performance in high-temperature environments, improving thermal fatigue resistance, and optimizing tribological properties has become increasingly critical. Brake disc materials have undergone substantial evolution, transitioning from traditional iron and steel to lightweight aluminum matrix composites and carbon matrix composites. While iron and steel benefit from mature manufacturing processes and proven reliability, their high mass density poses challenges in meeting the demands for lightweight and high-speed development in modern rail transit. Although aluminum matrix composites and carbon matrix composites offer advantages like low density and high heat capacity, they still face several technical challenges in practical applications. This paper outlines the key characteristics of train brake disc materials, emphasizing the application status and research progress of iron and steel, aluminum matrix composites, and carbon matrix composites. Additionally, it briefly introduces surface modification technologies for iron and steel brake discs, with the goal of providing insights and references to guide the innovation and development of train brake disc materials. Full article

The energy crisis and environmental pollution have driven the rapid development of new energy vehicles (NEVs). As a core technology for integrating electrification and intelligence in NEVs, the brake-by-wire (BBW) system has become a research hotspot due to its excellent braking energy recovery efficiency and precise active safety control performance. This paper provides a comprehensive review of the research progress in BBW technology for NEVs and provides a forward-looking perspective on its future development. First, the types and structures of the BBW system are introduced, and the development history and representative products are systematically reviewed. Next, this paper focuses on key technologies, such as the design and modeling methods of the BBW system, braking force optimization and distribution strategies, precise actuator control, multi-system coordination, driver operation perception, intelligent decision-making, personalized control, and fault diagnosis and fault-tolerant control. Finally, the main challenges faced in the research of BBW technology for NEVs are analyzed, and future development directions are proposed, providing insights for the optimization designs and industrial application of the BBW system in the future. Full article

Advanced driving assistance systems (ADASs) provide rich data on vehicles and their surroundings, enabling early detection and warning of driving risks. This study proposes a short-term risk prediction method based on in-vehicle perception data, aiming to support real-time risk identification in ADAS environments. A variable sliding window approach is employed to determine the optimal prediction window lead length and duration. The method incorporates Monte Carlo simulation for threshold calibration, Boruta-based feature selection, and multiple machine learning models, including the light gradient-boosting machine (LGBM), with performance interpretation via SHAP analysis. Validation is conducted using data from 90 real-world driving sessions. Results show that the optimal prediction lead time and window length are 1.6 s and 1.2 s, respectively, with LGBM achieving the best predictive performance. Risk prediction effectiveness is enhanced when integrating information across the human–vehicle–road environment system. Key features influencing prediction include vehicle speed, accelerator operation, braking deceleration, and the reciprocal of time to collision (TTCi). The proposed approach provides an effective solution for short-term risk prediction and offers algorithmic support for future ADAS applications. Full article

Large-inertia pump-controlled grinding machines experience significant energy loss and potential hydraulic shock during frequent high-speed table reciprocation. Traditional control methods often neglect to address efficient energy recovery during the dynamic commutation phase. This study proposes and investigates a dual-mode synergistic energy recovery system that combines motor regeneration and accumulator storage for pump-controlled grinders. The primary focus of this study is on developing a maximum regenerative energy commutation control strategy. A mathematical model of the system was established, and extensive simulations were performed to analyze the energy recovery process under varying load mass, initial velocity, and leakage coefficient conditions. Machine learning models were compared for predicting the peak time of total recovered energy, with a neural network (NN) demonstrating superior accuracy (R² ≈ 0.99997). An adaptive commutation strategy was designed, utilizing the NN prediction corrected by a confidence score based on historical and test data ranges, to determine the optimal moment for initiating reverse motion. The strategy was validated using Simulink–Amesim co-simulation and experiments conducted on a 10-ton test bench. The results show that the proposed strategy effectively maximizes energy capture; experiments indicate a 14.3% increase in energy recovery efficiency and a 25% reduction in commutation time compared to a fixed timing approach. The proposed commutation strategy also leads to faster settling to steady-state velocity and smoother operation, while the accumulator demonstrably reduces pressure peaks. This research provides a robust method for enhancing energy efficiency and productivity in pump-controlled grinding applications by improving regenerative braking control through a predictive commutation strategy. Full article