Search Results (2,826)

This paper presents a simulation-based methodology for automated tuning of a triple-loop controller (steering, throttle, and braking) for a Dallara single-seater race car. The approach targets on-track driving at handling limits, where strong nonlinearities and coupled dynamics dominate, treating the vehicle as a black box. Five controller gains are optimized via derivative-free pattern search, using reference trajectories from a professional driver in a Driver-in-the-Loop (DiL) simulator. Human-likeness is promoted by penalty terms on state and control trajectories while maximizing distance over a fixed horizon as a proxy for lap-time reduction. The application uses a high-fidelity multibody vehicle model with realistic tire, suspension, and actuator dynamics in the DiL environment, rather than simplified single-track representations. Contributions are: (i) effective application of derivative-free optimization to complex, high-dimensional, black-box vehicle systems; and (ii) a systematic, reproducible procedure for automatic tuning of controller parameters with a predetermined architecture to reproduce a professional driver’s performance and embed human-likeness. Optimization required approximately 2.4 h. Results show that the optimized controller improves track coverage by 63.6 m (1.1% increase) compared to manual tuning while maintaining a realistic driving style, offering a more systematic and reliable solution than manual, trial-and-error calibration. Full article

There are some methods based on the railway power conditioner (RPC) that can address the imbalance issue, improve load fluctuation, and manage the regenerative braking energy (RBE) of the traction power supply system in high-speed railways. However, the coupling of imbalance compensation and energy storage is a problem in the RPC method. Therefore, a novel decoupling control method is proposed in this paper. The topology of the method is based on a three-phase converter, and the energy storage unit is connected to the DC side of the converter. A decoupling possibility and principle analysis is carried out. The mechanism of the proposed method in coping with different working conditions of high-speed railways is introduced in detail. Then, a capacity analysis and the control method are presented. According to the theoretical analysis, while the traditional RPC requires no extra capacity under single-task operations, its required capacity increases by 15.47% under typical hybrid conditions and can even surge by over 30% under severe coupling scenarios, to achieve the same effect as the proposed decoupled method. Finally, simulations and experiments are carried out to verify the effectiveness and flexibility of the novel method. Full article

This study presents a comparative Failure Modes and Effects Analysis (FMEA) of electro-hydraulic braking (EHB) and electro-mechanical braking (EMB) systems within brake-by-wire architectures. The analysis integrates both the conventional Risk Priority Number (RPN) approach and the AIAG–VDA Action Priority (AP) methodology, enabling a structured comparison of risk prioritization strategies applied to identical failure modes. A consistent system-level framework is developed to harmonize severity (S), occurrence (O), and detection (D) assessments across both architectures, allowing direct evaluation of methodological differences. The results demonstrate systematic divergences between RPN and AP approaches, particularly in high-severity scenarios, where AP provides more safety-oriented prioritization. The study further identifies key limitations of traditional RPN-based evaluation in safety-critical systems and highlights the advantages of rule-based prioritization frameworks. In addition, corrective measures are proposed and their impact on occurrence and detection ratings is quantified, illustrating practical pathways for risk reduction. Beyond methodological comparison, the work introduces a novel integration of reliability engineering with advanced manufacturing strategies, demonstrating how laser and plasma-based surface engineering can mitigate failure mechanisms by reducing occurrence and improving system robustness. The proposed approach establishes a conceptual and physically grounded bridge between system-level risk assessment and material-level optimization, contributing to the development of more reliable next-generation brake-by-wire systems. Full article

To address the emergency obstacle-avoidance problem of intelligent vehicles on structured roads, this paper proposes an integrated planning and control method that combines an improved Artificial Potential Field (APF) with fuzzy Model Predictive Control (MPC). Different from a direct APF + MPC combination, the planning layer introduces a braking-distance threshold, an effective obstacle-influence boundary, and sinusoidal shape factors to reshape the obstacle repulsive field and alleviate local-minimum behavior. A seventh-order polynomial smoothing strategy is then adopted to generate a reference path with higher-order continuity. For trajectory tracking, a fuzzy adaptive MPC controller adjusts the prediction horizon and control horizon online according to lateral error, while a fuzzy PID controller regulates longitudinal speed. MATLAB/Simulink and CarSim co-simulation results in single-static, double-static, and double-dynamic obstacle scenarios show that the proposed method can generate smoother trajectories and achieve more stable tracking, thereby improving obstacle-avoidance safety and ride comfort. In the double-static scenario, the peak lateral error is reduced from about 0.7 m to within 0.1 m, while in the double-dynamic scenario the longitudinal speed is maintained within 78–80 km/h instead of dropping to about 67 km/h under the baseline controller. The study provides a practical technical framework for integrated decision-planning-control design in structured-road intelligent vehicles. Full article

Reducing traction energy in urban rail systems while preserving safety, punctuality, and passenger comfort remains challenging. Additionally, route-level tram studies that train deep reinforcement learning (DRL) policies using Operational Train Monitoring Recorder (OTMR) logs and benchmark them across multiple objectives remain limited. This study develops and evaluates a Human-Assisted Deep Reinforcement Learning (HADRL) framework for multi-objective tram control in an OTMR-grounded simulation. Two HADRL agents were trained using a human-assistance action mapping: a standard Proximal Policy Optimisation (PPO) baseline and a recurrent, history-augmented PPO. Their performance was compared against that of four human drivers using indices for speed-limit compliance, schedule deviation, traction energy, jerk-based comfort, and stopping accuracy. These performance measures were aggregated using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with both equal and entropy-derived weights. Both HADRL agents reproduce the characteristic accelerate–coast–brake driving pattern, reduce traction energy relative to all human baselines, and achieve near-complete speed-limit compliance, all while remaining within the specified schedule-deviation and comfort thresholds. TOPSIS yields identical rankings under both weighting schemes, with Multi-Objective Tram Operation Non-Stationary Proximal Policy Optimisation (MOTO-NSPPO, a recurrent, history-augmented PPO) ranked first and PPO second. Full article

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article

(1) Background: Military personnel are required to perform high-intensity actions and tactical tasks under external load, which increases system weight and alters movement mechanics. Understanding how these loaded conditions influence neuromuscular performance is essential for informing physical preparation and readiness monitoring. This study quantified the effects of tactical equipment on countermovement jump (CMJ) and countermovement rebound jump (CMRJ) force–time characteristics in active military personnel and evaluated the within-session reliability of these metrics under loaded and unloaded conditions; (2) Methods: Eighteen male soldiers performed CMJ and CMRJ assessments on dual force plates (1000 Hz) under unloaded and loaded conditions (standardized tactical equipment: 10.6 ± 1.18 kg). Force–time variables were categorized as strategy (phase durations, countermovement depth), driver (mean braking and propulsive force), and outcome (jump height, jump momentum, and modified reactive strength index; mRSI) metrics; (3) Results: CMJ outcome and driver metrics demonstrated good to excellent reliability under load (ICC ≥ 0.87; CV ≤ 8.4%), whereas CMRJ outcome variables showed reduced reliability and greater variability. Loaded conditions reduced jump height and mRSI in both CMJ and CMRJ (p < 0.05), while jump momentum and absolute mean force production increased, whereas force production relative to body mass decreased. During the CMJ (slow-SSC), participants exhibited longer braking and propulsive phase durations, indicating a temporal change in movement strategy under load, whereas CMRJ (fast-SSC) force–time characteristics showed increased contact time and reduced rebound metrics; (4) Conclusions: Overall, fast stretch–shortening cycle tasks appear more sensitive to loading conditions, whereas the CMJ provides a more robust and reliable assessment for monitoring neuromuscular performance in military personnel, particularly when considering both absolute and relative force responses. Full article

The safety and reliability of railway wagon operations largely depend on the timely detection of degradation in safety-critical components such as axle bearings, wheelsets, and braking systems. Conventional maintenance strategies based on fixed inspection intervals are often inadequate for capturing the actual operating conditions of wagon components, leading to delayed fault detection or unnecessary maintenance actions. To address these limitations, this paper proposes a sensor-based health assessment framework for the continuous monitoring of railway wagons under operational conditions. The proposed framework integrates multi-sensor data acquisition, systematic signal preprocessing, feature-based health indicator construction, and temporal degradation analysis to evaluate component health in real time. A safety-oriented decision logic is employed to classify operating conditions and generate reliable alerts while minimizing false detections caused by transient disturbances. The effectiveness of the proposed approach is validated using a publicly available run-to-failure bearing dataset that exhibits degradation characteristics similar to those observed in railway wagon axle bearings. Experimental results demonstrate that the proposed framework achieves improved classification accuracy, higher detection reliability, reduced false alarm rates, and lower detection latency compared to representative existing condition monitoring approaches. In addition, the computational efficiency of the proposed model confirms its suitability for real-time deployment. The results indicate that the proposed health assessment framework provides a practical and reliable solution for safety-critical railway wagon monitoring and forms a strong foundation for future extensions toward predictive maintenance and remaining useful life estimation. Full article

Liquefied petroleum gas (LPG) is a widely available alternative fuel, easily stored in liquid form, capable of displacing diesel fuel in compression-ignition engines. Bio-LPG extends this pathway because it is a renewable drop-in form of LPG; its distinguishing advantage is not a different in-cylinder combustion chemistry, but a lower life-cycle greenhouse-gas intensity that depends on feedstock and production route. This review, therefore, combines a systematic synthesis of CI-engine LPG combustion evidence with a Bio-LPG transition perspective. A PRISMA-guided search of major databases (2000–2025) yielded 47 studies with matched diesel baseline. Evidence was categorized by LPG utilization pathway, distinguishing between fumigation, gaseous port injection, and in-cylinder LPG direct injection (gaseous or liquid), alongside engine class, pilot fuel fraction, and key operating parameters (injection timing/quantity, intake conditioning, exhaust gas recirculation (EGR), and boost). Data were normalized as percentage deviations relative to diesel and synthesized across standardized load bins (25/50/75/100%). Among studies reporting nitrogen oxides (NO_x), 20 of 37 showed net reductions, while results in 12 studies were load-dependent; particulate matter (PM), smoke, and soot indicators decreased in 17 of 27 cases. While intake-path strategies generally reduced NO_x and smoke, they often increased CO and HC emissions at low loads. The limited emerging liquid-phase direct-injection evidence shows the closest diesel-like efficiency response, although the evidence base remains limited. Overall, the engine-level findings identify the most promising LPG/Bio-LPG deployment pathways, while the specific additional climate benefit of Bio-LPG lies in its lower well-to-wheel greenhouse-gas intensity. Full article

This study quantifies a critical winter safety hazard caused by lateral heterogeneity of skid resistance: under non-uniform snow and ice removal, the friction coefficient in edge lanes and near barrier guardrails can be 2–5 times lower than in the central part of the carriageway, creating conditions prone to loss of control during braking and lane changes. Field measurements of friction coefficient and macrotexture were conducted on highways of different technical categories with asphalt concrete and cement concrete pavements in Kazakhstan’s continental climate. Long-term monitoring showed that, over three years of operation, texture peak height decreases by 22–33%, depending on traffic intensity and heavy-vehicle share, leading to a gradual reduction in friction. Predictive assessments of skid-resistance deterioration and braking distance calculations for passenger cars and heavy vehicles under different friction levels were performed. The results support the need for regular texture monitoring, explicit consideration of across-width friction heterogeneity in accident analysis, and targeted improvements in winter maintenance practices, particularly in edge zones adjacent to barriers. Full article

To tackle traditional synthetic brake pads’ friction instability and performance degradation at high speeds, as well as the costly and time-consuming empirical formula optimization, a multi-stage synergistic optimization (MSSO) framework driven by two-stage machine learning is proposed in this study. The novelty lies in integrating Pearson correlation filtering with Gaussian noise for data enhancement, employing a hybrid sparrow search algorithm-gray neural network model for dataset expansion, and utilizing a red-billed blue magpie optimization-backpropagation neural network for high-precision multi-target prediction. Experimental verification shows that brake pads manufactured using the optimized formulations exhibit improved average friction coefficient and wear rate, with reduced error compared to traditional methods. The friction characterization results of composite brake pads show the features of optimized composite brake pads at the surface microscopic level. This provides an efficient solution for developing lightweight brake materials for high-speed trains. Full article

Collisions between motorcycles and car doors that are being opened are common, preventable accidents that can result in fatalities. A critical limitation of safety advancements in both cars and motorcycles is high cost associated with the use of radar sensors. In this study, a deep learning model was integrated into an inexpensive and camera-utilizing automatic braking assistance system for motorcycles to enhance braking performance and alert motorcyclists to avoid collisions. This research involved two stages: (1) the training of a deep learning model for detecting car door states and (2) the design of safety mechanisms for selecting appropriate braking intensity and front braking ratio values on the basis of the model’s output, time-to-collision, the rider’s braking action, and the initial braking speed, in order to achieve optimal braking performance. Specifically, the YOLOv12s object detection model showed high performance in predicting the states of car doors, exhibiting precision, recall, and mean average precision values of 90.5%, 80.6%, and 87.8%, respectively. The braking intensity of the system was set to 0%, 25%, 50%, or 100% in scenarios involving opening states of the car door (closed, small, medium, or large opening), time-to-collision values, and the rider’s braking action. The optimal front braking ratio function was determined based on the initial braking speed to achieve the optimal braking performance. At an initial braking speed of 60 km/h, the braking stroke under a front braking ratio of 45% was 35.61% and 13.37% shorter than those under front braking ratios of 20% and 60%, respectively. The proposed braking assistance system can feasibly be deployed in the real world because it can respond within a safe time window under the conditions studied, which is approximately 0.5 s. However, further refinement is required, including improvement of the robustness of the object detection model through the collection of a larger and more diverse dataset, experimental measurement of front braking ratios to determine the optimal braking performance in real scenarios, and design of a physical actuator to control braking intensity and the front braking ratio in real time. Full article

The decarbonization of marine transport requires the wider use of alternative low-carbon fuels that can be applied in existing compression ignition (CI) engines without major modifications. Hydrotreated vegetable oil (HVO) is considered a promising renewable drop-in fuel due to its favorable physicochemical properties and high cetane number. This study investigates the influence of neat HVO and its blends with conventional diesel fuel on the combustion characteristics, energy, and emission indicators of a CI engine operating under different load conditions and exhaust gas recirculation (EGR) ratios. Experimental tests were carried out on a four-cylinder CI engine at constant speed and variable load using diesel fuel (D100), HVO100, and their blends (D80_HVO20 and D50_HVO50). In-cylinder pressure measurements and combustion analysis were performed using AVL instrumentation and AVL BOOST software. The results show that increasing the HVO fraction slightly advances combustion phasing and increases maximum in-cylinder pressure by approximately 4–5%. The use of HVO was found to reduce brake-specific fuel consumption by up to 3.4% and increase brake thermal efficiency by about 1.9%, although volumetric fuel consumption increases due to the lower fuel density. In addition, higher HVO content significantly reduces smoke opacity by up to 42% and decreases CO₂ emissions by 4.7–6.3%, while the influence on NO_x emissions depends on the applied EGR strategy. The results indicate that HVO and its blends can be effectively applied in CI engines; however, optimal performance and emission characteristics require appropriate calibration of EGR rate and fuel injection timing. Full article

With the opening of complex service routes, the importance of the service performance of brake pads under long slope braking conditions is increasing. It is necessary to analyze the slope braking adaptability of current brake pad products. This work takes the copper-based powder metallurgy brake pads of a certain in-service high-speed train as the research object and conducts friction and wear behavior tests of the brake pads based on a full-scale brake test bench. Through microscopic observation and damage analysis, the differences in friction and wear behavior of the brake pads under stop braking and slope braking conditions are compared, revealing the wear mechanism and damage evolution characteristics of the brake pads. The results show that under the impact of high speed, high braking force, and severe thermal load in the stop braking conditions, the uneven wear of brake pads is high, and the eccentric wear of friction blocks is affected by both the friction radius and friction direction. The friction surface has a large number and size of damages, and the stability of the friction interface is poor. The brake pad exhibits a composite wear mechanism dominated by abrasive wear and brittle fracture induced exfoliation. In the slope braking condition, under the action of low speed, low braking force, and long-term stable thermal load, the uneven wear of the brake pads is relatively low, the surface damage size is small, and the friction block only has eccentric wear along the friction direction. The brake pad mainly initiates cracks along the interface of the components, which propagate parallel to the friction surface, exhibiting a progressive delamination and flaking exfoliation mechanism with a low wear rate. Although the friction interface of the brake pad is relatively stable under slope braking conditions, the cumulative delamination wear of the brake pads under long-term braking action needs further attention. Full article