Search Results (69)

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

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

Nowadays, there are many studies that have been conducted in order to reduce the emissions of modern reciprocating engines without, at the same time, having a negative impact on the performance characteristics. One method to accomplish that is by using ethanol-supplemented fuels instead of conventional gasoline. On the other side of the spectrum, spark timing is one of the most important parameters that affects the combustion mechanism inside a reciprocating engine and is basically controlled by the ignition advance of the engine. Therefore, the main purpose of this study is to investigate the effect of spark timing alteration on the performance characteristics and emissions of a modern reciprocating, naturally aspirated, aircraft SI engine (i.e., ROTAX 912s), operated under four different engine operating points (i.e., combination of engine speed and throttle opening), by using ethanol-supplemented fuel. The implementation of the aforementioned method is achieved through the use of an advanced simulating software (i.e., GT-POWER), which provides the user with the possibility to completely design a piston engine and parameterize it, by using a comprehensive single-zone phenomenological model, for any operating conditions in the entire range of its operating points. The predictive ability of the designed engine model is evaluated by comparing the results with the experimental values obtained from the technical manuals of the engine. For all test cases examined in the present work, the results are affiliated with important performance characteristics, i.e., brake power, brake torque, and brake-specific fuel consumption, as well as specific NO and CO concentrations. Thus, the primary objectives of this study were to examine and evaluate the results of the combination of using ethanol-supplemented fuel instead of gasoline and the alteration of the spark timing, to asses their effects on the basic performance characteristics and emissions of the aforementioned type of engine. By examining the results of this study, it is revealed that the increase in the ethanol concentration in the gasoline–ethanol fuel blend combined with the increase in the ignition advance might be an auspicious solution in order to meliorate both the performance and the environmental behavior of a naturally aspirated SI aircraft piston engine. In a nutshell, the outcoming results of this research show that the combination of the two methods examined may be a valuable solution if applied to existing reciprocating SI engines. Full article

Autonomous navigation for micro electric vehicles (micro EVs) operating in semi-structured environments—such as university campuses and industrial parks—requires solutions that are cost-effective, low in complexity, and robust. Traditional autonomous systems often rely on high-definition maps, multi-sensor fusion, or vision-based SLAM, which demand expensive sensors and high computational power. These approaches are often impractical for micro EVs with limited onboard resources. To address this gap, a real-world autonomous navigation system is presented, combining RTK-GNSS and 2D LiDAR with a real-time trajectory scoring algorithm. This configuration enables accurate path following and obstacle avoidance without relying on complex mapping or multi-sensor fusion. This study presents the development and experimental validation of a low-speed autonomous navigation system for a micro electric vehicle based on GNSS-RTK localization and real-time obstacle avoidance. The research achieved the following three primary objectives: (1) the development of a low-level control system for steering, acceleration, and braking; (2) the design of a high-level navigation controller for autonomous path following using GNSS data; and (3) the implementation of real-time obstacle avoidance capabilities. The system employs a scored predicted trajectory algorithm that simultaneously optimizes path-following accuracy and obstacle evasion. A Toyota COMS micro EV was modified for autonomous operation and tested on a closed-loop campus track. Experimental results demonstrated an average lateral deviation of 0.07 m at 10 km/h and 0.12 m at 15 km/h, with heading deviations of approximately 3° and 4°, respectively. Obstacle avoidance tests showed safe maneuvering with a minimum clearance of 1.2 m from obstacles, as configured. The system proved robust against minor GNSS signal degradation, maintaining precise navigation without reliance on complex map building or inertial sensing. The results confirm that GNSS-RTK-based navigation combined with minimal sensing provides an effective and practical solution for autonomous driving in semi-structured environments. Full article

Regenerative braking systems (RBS enhance energy efficiency and range in electric vehicles (EVs) by recovering kinetic energy during braking for storage in batteries or alternative systems. This literature review examines RBS advancements from 2005 to 2024, focusing on system design, control strategies, energy storage technologies, and the impact of external and kinematic factors on recovery efficiency. Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. Technologies such as brake-by-wire and in-wheel motors improve safety and stability, with the latter excelling in all-wheel-drive setups over single-axle configurations. Hybrid Energy Storage Systems (HESS), combining batteries with supercapacitors or kinetic accumulators, address power peak demands, though cost and complexity limit scalability. Challenges include high computational requirements, component reliability in harsh conditions, and lack of standardized testing. Research gaps involve long-term degradation, autonomous vehicle integration, and driver behavior effects. Future work should explore cost-effective HESS, robust predictive controls for autonomous EVs, and standardized frameworks to enhance RBS performance and support sustainable transportation. Full article

In this paper, a novel technical route, namely combining the low-pressure exhaust gas recirculation (LP-EGR) and variable compression ratio (VCR), is proposed to address the inferior fuel economy for marine dual-fuel engines of low-pressure gas injection in diesel mode. To validate the applicability of the proposed technical route, firstly, a zero-dimensional/one-dimensional (0-D/1-D) engine simulation model with a predictive combustion model DI-Pulse is established using GT-Power. Then, parametric investigations on two LP-EGR schemes, which is implemented with either a back-pressure valve (LP-EGR-BV) or a blower (LP-EGR-BL), are performed to qualitatively identify the combined impacts of exhaust gas recirculation (EGR) and compression ratio (CR) on the combustion process, turbocharging system, and nitrogen oxides (NOx)-brake specific fuel consumption (BSFC) trade-offs. Finally, an optimization strategy is formulated, and an optimization program based on response surface methodology (RSM)–particle swarm optimization (PSO) is designed with the aim of improving fuel economy while meeting Tier III and various constraint conditions. The results of the parametric investigations reveal that the two LP-EGR schemes exhibit opposite impacts on the turbocharging system. Compared with the LP-EGR-BV, the LP-EGR-BL can achieve a higher in-cylinder pressure level. NOx-BSFC trade-offs are observed for both LP-EGR schemes, and the VCR is confirmed to be a viable approach for mitigating the penalty on BSFC caused by EGR. The optimization results reveal that for LP-EGR-BV, compared with the baseline engine, the optimized BSFC decreases by 10.16%, 11.95%, 10.32%, and 9.68% at 25%, 50%, 75%, and 100% maximum continuous rating (MCR), respectively, whereas, for the LP-EGR-BL scheme, the optimized BSFC decreases by 10.11%, 11.93%, 9.93%, and 9.58%, respectively. Furthermore, the corresponding NOx emissions level improves from meeting Tier II regulations (14.4 g/kW·h) to meeting Tier III regulations (3.4 g/kW·h). It is roughly estimated that compared to the original engine, both LP-EGR schemes achieve an approximate reduction of 240 tons in annual fuel consumption and save annual fuel costs by over USD 100,000. Although similar fuel economy is obtained for both LP-EGR schemes, LP-EGR-BV is superior to LP-EGR-BL in terms of structure complexity, initial cost, maintenance cost, installation space requirement, and power consumption. The findings of this study provide meaningful theoretical supports for the implementation of the proposed technical route in real-world engines. Full article

This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power allocation during takeoff, cruise, landing, and ground operations, ensuring optimal energy utilization while minimizing losses. A MATLAB-based simulation framework is developed to evaluate key performance metrics, including power demand, state of charge (SOC), system efficiency, and energy recovery through regenerative braking. The findings show that by optimizing renewable energy collecting, minimizing battery depletion, and dynamically controlling power sources, AI-based predictive control dramatically improves energy efficiency. While carbon footprint assessment emphasizes the environmental advantages of using renewable energy sources, SOC analysis demonstrates that regenerative braking prolongs battery life and lowers overall energy use. AI-optimized energy distribution also lowers overall operating costs while increasing reliability, according to life-cycle cost assessment (LCA), which assesses the economic sustainability of important components. Sensitivity analysis under sensor noise and environmental disturbances further validates system robustness, demonstrating that efficiency remains above 84% even under adverse conditions. These findings suggest that AI-enhanced hybrid propulsion can significantly improve the sustainability, economic feasibility, and real-world performance of future flying car systems, paving the way for intelligent, low-emission aerial transportation. Full article

Considering that slow steaming requires low engine power, which impedes maneuverability under severe sea conditions, the International Maritime Organization (IMO) provides guidelines for the minimum propulsion power (MPP) required to maintain ship maneuverability in adverse conditions. This study focused on the characteristics of self-propulsion factors in the context of MPP assessment to enhance MPP prediction accuracy. Overload tests were conducted at low speeds of advance, considering added resistance in adverse conditions. Moreover, propeller open-water tests were conducted corresponding to propeller flow with low Reynolds numbers to investigate their effect on self-propulsion factors. In addition, computational fluid dynamics (CFD) simulations were conducted to analyze physical phenomena such as the flow field and pressure distribution under model test conditions. The results indicated that the thrust deduction factor was lower than that given in the guidelines, whereas the wake fraction was higher at the required forward speed of 2 knots. The MPP assessment in this study revealed that the required brake power was 4–5% lower than that given in the guidelines, indicating that the guidelines need reviewing for a more reliable assessment. Full article

This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)—enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal role in shaping next-generation, energy-efficient EVs. Full article

Induction motor (IM) drives are considered one of the important technologies in modern industry. Several industrial applications, such as material handling and food and beverage applications, are driven and operated by modern AC drives. Moreover, modern electric transportation systems such as EVs and e-trucks are based on AC drives. Recently, high-efficiency IM drive systems have been studied as a major opportunity to reduce energy and fuel consumption. This article addresses the recent trends and advancement in high-efficiency IM drives during a particular period (2017–2024), including the development of high-efficiency motors, the utilization of efficient wide bandgap (WBG) semiconductor devices for inverter topology, and commonly used control strategies to achieve high-performance drives. Moreover, the article addresses several manufacturers of industrial IM drives and the corresponding adopted control techniques in their products. A comparison of these control techniques, including their pros and cons, has been conducted as well. Full article

The present work compares the performance and emissions of a compression ignition (CI) engine using dual-mode LPG at varying flow rates and an oxygenated biodiesel mix (B20). The experimental investigation is carried out on LPG flow rates (0.1, 0.3, and 0.5 kg/h) and replacing the diesel with oxygenated B20, affecting engine performance and emissions under various load circumstances while maintaining engine speed. The study demonstrates the potential of the artificial neural network (ANN) in accurately forecasting the performance and emission characteristics of the engine across different operating conditions. The ANN model’s high accuracy in correlating experimental results with predicted outcomes underscores its potential as a dependable instrument for optimizing fuel parameters. The results show that LPG and oxygenated B20 balance engine performance and emissions, making CI engine functionality sustainable. A biodiesel blend containing diethyl ether (B20 + 2%DEE) exhibits slightly reduced brake thermal efficiency (BTE) at lower brake power (BP); however, it demonstrates advantages at higher BP, with diethyl ether contributing to improved ignition quality. The analysis indicates that the average NO_x emissions for B20 + 2%DEE at flow rates of 0.1 kg/h, 0.3 kg/h, and 0.5 kg/h are 29.33%, 28.89%, 48.05%, and 37.48%, respectively. Consequently, selecting appropriate fuel and regulating the LPG flow rate is critical for enhancing thermal efficiency in a dual-fuel engine. Full article

Brake pad wear forecasting, due to its complex nature, is very difficult to describe using engineering formulas. Therefore, the aim of this publication is to create high-quality brake pad wear forecasts based on three stochastic quantitative models based on multiple regression models (linear model, inverted linear model, and power model). The matrix of explanatory variables was extracted from the controllers of 29 vehicles: A—the driver’s style of using the brake pedal specified on a 4-point scale and B—the number of vehicle load ranges specified on a 5-point scale. Methodology: A matrix of explanatory variables was obtained over a 2-year period from trucks carrying oversize loads via OBD2 socket. The trucks operated under similar operating conditions. The created models were verified in terms of their fit to the source data and by analyzing the residuals of the models. It should be emphasized that only the linear model met all the required criteria. The inverted linear and power-law models were rejected. Results: The verified linear model is characterized by very small MAPE errors. The model was validated on 4 trucks and the brake pad wear prediction errors ranged from −0.39% to 7.03%. Full article

The mechatronic industry is currently subject to huge changes challenging it to offer products matching individual customer requirements at competitive prices. The design of such products calls for sophisticated and complex components integration following different technologies. Since we are on the cusp of the Fourth Industrial Revolution, in which the world of mechatronic production, network connectivity, the Internet of Things, and cyber-physical systems are correlated, the complexity of these systems increases exponentially, and we are talking about advanced mechatronic systems. To assist these changes, various methods, sweeping all project phases, are used by business houses. Predictive dependability assessment in the earlier design stage is considered a powerful metric used to evaluate the performances of different kinds of mechatronic products before the production phase. Altogether, dependability analysis ties the design directly to the desired functionality, operability, and integrity of the system. This paper explores an approach to assessing the dependability attributes, reliability, availability, and maintainability (RAM), of repairable mechatronic systems based on timed colored Petri nets and a Monte Carlo simulation, integrating simultaneously diverse components technologies: mechanical, electronic, and software. The proposed approach is tested taking the case of a regenerative braking system. The methodology appears to be efficient for evaluating predictive RAM indicators (MTTFF, MTTR, MTBF…) for the whole system and for each individual component separately. Full article

To reduce the cost of small wind turbines, a prototype of a butterfly wind turbine (6.92 m in diameter), a small vertical-axis type, was developed with many parts made of extruded aluminum suitable for mass production. An overspeed control system with movable arms that operated using centrifugal and aerodynamic forces was installed for further cost reduction. Introducing this mechanism eliminates the need for large active brakes and expands the operating wind speed range of the wind turbine. However, although the mechanism involving the use of only bearings is simple, the violent movement of the movable arms can be a challenge. To address this in the present study, dampers were introduced on the movable arm rotation axes to improve the movement of the movable arms. To predict the behavior of a movable arm and the performance of the wind turbine with the mechanism, a simulation method was developed based on the blade element momentum theory and the equation of motion of the movable arm system. A comparison of experiments and predictions with and without dampers demonstrated qualitative agreement. In the case with dampers, measurements confirmed the predicted increase in the rotor rotational speed when the shorter ailerons installed perpendicularly to the movable arms were used to achieve the inclination. Field experiments of the generated power at a wind speed of 6 m/s (10 min average) showed relative performance improvements of 11.4% by installing dampers, 91.3% by shortening the aileron length, and 57.6% by changing the control target data. The movable arm system with dampers is expected to be a useful device for vertical-axis wind turbines that are difficult to control. Full article

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance. Full article