Search Results (580)

Although particulate matter (PM) pollution from vehicles’ exhaust has decreased significantly over the years, the contribution from non-exhaust sources (brakes, tyres) has remained at the same levels. In the European Union (EU), Euro 7 regulation introduced PM limits for vehicles’ brake systems. Regenerative braking, i.e., recuperation of the deceleration kinetic and potential energy to the vehicle battery, is one of the strategies to reduce the brake emission levels and improve vehicle efficiency. According to the regulation, the shares of friction and regenerative braking can be determined with actual testing of the vehicle on a chassis dynamometer. In this study we tested the regenerative capabilities of a plug-in hybrid vehicle, both in the laboratory and on the road, under different protocols (including both smooth and aggressive braking) and covering a wide range of driving conditions (urban, rural, motorway) over 10,000 km of driving. Good agreement was obtained between laboratory and on-road tests, with the use of the friction brakes being on average 7% and 5.3%, respectively. However, at the same time it was demonstrated that the friction braking share can vary over a wide range (up to around 30%), depending on the driver’s behaviour. Full article

This paper proposes a multibody model calibration method for vehicle misuse testing. During misuse tests conducted at high driving speeds, the vehicle’s responses can become highly nonlinear due to certain key model parameters. Direct calibration using a complex multibody model is time-consuming and unstable, as it may fail or diverge due to improper settings of the model parameters. Therefore, a modified quarter-vehicle model is proposed for the analytical calibration of these nonlinear parameters by introducing an additional constraint on the sprung mass to recover the restoring force. The new model features only two degrees of freedom and incorporates key nonlinear parameters, including the suspension’s stiffness and the wheel’s center mass. It is suitable for misuse tests involving tire detachment at high driving speeds. The detailed analytical calibration procedure for the nonlinear parameters is deduced and subsequently validated through numerical simulation using these parameters. When the parameters are sufficiently close to the actual ones or linearly related to the responses, an optimization method such as the least squares method can be applied, along with simulations using complex models in commercial software. Full article

This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating real-world resistance profiles on a dynamometer. Energy usage data were measured using On-Board Diagnostics II (OBD-II) and dynamometer measurements to assess power flow from the battery to the wheels. The results reveal that OBD-II consistently recorded higher cumulative energy usage, particularly under urban driving conditions, highlighting limitations in dynamometer responsiveness to transient loads and regenerative events. Notably, the WLTP low-speed cycle exhibited a significantly lower efficiency of 62.42%, with nearly half of the battery energy consumed by non-propulsion systems. In contrast, the EPA cycle demonstrated consistently higher efficiencies of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, high-speed efficiencies between WLTP and EPA were nearly identical, despite differences in total energy consumption. These findings underscore the importance of aligning test protocols with actual driving conditions and demonstrate the effectiveness of combining coast down data with real-time diagnostics for robust EV performance assessments. Full article

Due to the complexity of the unstructured environment and the high-level requirement of smoothness when a tracked transportation vehicle is traveling, making the vehicle travel as safely and smoothly as when a skilled operator is maneuvering the vehicle is a critical issue worth studying. To this end, this study proposes a trajectory-planning method for human-like maneuvering. First, several field equipment operators are invited to manipulate the model vehicle for obstacle avoidance driving in an outdoor scene with densely distributed obstacles, and the manipulation data are collected. Then, in terms of the lateral displacement, by comparing the similarity between the data as well as the curvature change degree, the data with better smoothness are screened for processing, and a dataset of human manipulation behaviors is established for the training and testing of the trajectory-planning network. Then, using the dynamic parameters as constraints, a two-stage planning approach utilizes a modified deep network model to map trajectory points at multiple future time steps through the relationship between the spatial environment and the time series. Finally, after the experimental test and analysis with multiple methods, the root-mean-square-error and the mean-average-error indexes between the planned trajectory and the actual trajectory, as well as the trajectory-fitting situation, reveal that this study’s method is capable of planning long-step trajectory points in line with human manipulation habits, and the standard deviation of the angular acceleration and the curvature of the planned trajectory show that the trajectory planned using this study’s method has a satisfactory smoothness. Full article

The objective of this study was to analyze the effects of variables such as pre-crash emergency braking and reclined posture on human injuries in autonomous vehicle collisions using an active human model and through crash analysis. To achieve this, the MADYMO (MAthematical DYnamic MOdels) active human model was validated for predicting occupant responses during pre-crash emergency braking. Its biofidelity during crash conditions was also validated. Additionally, the model was validated under component-level impact conditions to ensure its suitability for predicting occupant injuries. Two autonomous vehicle-relevant crash scenarios reconstructed based on actual accident conditions were selected. Variations in collision conditions, such as collision angles, overlaps, and relative collision speeds, were applied to selected crash scenarios. A finite element vehicle-to-vehicle crash analysis was performed to obtain the crash pulse. Using the validated crash analysis model, a parametric simulation study was conducted by applying variations to parameters such as emergency braking, seat-related parameters, and muscle activity. Finally, the impact of each variable on injury risk was analyzed using the Wilcoxon rank sum test. Analysis results showed that a reclined posture and a seat track position located 300 mm rearward from the baseline seat track position had a significant impact on injuries. Evaluation results on the effects of these variables can contribute to the development of safety evaluation standards for autonomous vehicles, such as crash safety regulations, by crash safety assessment organizations. Full article

The electric vertical take-off and landing fixed-wing (FW-VTOL) unmanned aerial vehicle (UAV) combines the advantages of fixed-wing aircraft and multi-rotor aircraft. Based on the cell discharge characteristics and the power system features, this paper proposes a preliminary design and optimization method suitable for electric FW-VTOL UAVs. The purpose of this method is to improve the design accuracy of electric propulsion systems and overall parameters when dealing with the special power and energy requirements of this type of aircraft. The core of this method involves testing the performance data of the cell inside the battery pack, using small-capacity cells as the basic unit for battery sizing, thereby constructing a power battery performance model. Additionally, it establishes optimization design models for propellers and rotors and develops a brushless DC motor performance model based on a first-order motor model and statistical data, ultimately achieving optimized matching of the propulsion system and completing the preliminary design of the entire aircraft. Using a battery discharge model established based on real cell parameters and test data, the impact of the discharge process on battery performance is evaluated at the cell level, reducing the subjectivity of battery performance evaluation compared to the constant power/energy density method used in traditional battery sizing processes. Furthermore, matching the optimization design of power and propulsion systems effectively improves the accuracy of the preliminary design for FW-VTOL UAVs. A design case of a 30 kg electric FW-VTOL UAV is conducted, along with the completion of flight tests. The design parameters obtained using the proposed method show minimal discrepancies with the actual data from the actual aircraft, confirming the effectiveness of the proposed method. Full article

The variable camber wing (VCW) is a morphing wing design anticipated to enhance unmanned aerial vehicles’ (UAVs’) performance in flight through continuously changing shape. The performance of VCWs has been proven, but techniques for their integration, including aerodynamic analysis, mechanism synthesis, and structural tests, still lag in development at the conceptual design stage. Therefore, this research focuses on designing a variable camber wing, a key area for the advancement of morphing aircraft. Inspired by the high-lift capabilities of traditional aircraft devices but aiming for smoother airflow through continuous shape alteration, this research proposes a novel three-step design for a structurally integrated VCW. This approach begins with a critical aerodynamic analysis to determine wing shape adaptations across various flight conditions, followed by a mechanism synthesis phase to design a four-bar linkage that accurately approximates the desired trailing edge deflections by utilizing a variant of teaching–learning-based optimization. The objective is to minimize error between the intended and actual coupler link while adhering to design constraints for proper integration in the wing structure. Finally, structural analysis evaluates the skin’s ability to withstand operational loads and ensure the integrity of the VCW system. The design result demonstrates the success of this three-step approach to synthesizing a VCW mechanism that meets the defined aerodynamic (actual deflection of 9.1764°) and structural targets (maximum Von Mises stress of 81.5 MPa and maximum deflection of 0.073 m), paving the way for enhanced aircraft performance. Full article

The need for autonomous vehicles has started rising rapidly. Many autonomous technologies, such as Cruise Control, the self-parking system, and the emergency braking system, are implemented in contemporary cars. These systems do not make the car fully autonomous; however, they allow people to get used to the idea of self-driving cars. Due to a surge of interest in autonomous systems, the development of these technologies has begun. This paper presents a model of Adaptive Cruise Control with a control system, which consists of two PID regulators. Using two PID regulators provides the possibility of more advanced regulation characteristics than using the classical one-PID regulation system. One of them regulates the powertrain model, the other the braking system model. The simulations are carried out using a vehicle dynamic system, whose thrust is determined by a real engine maximum torque curve that is approximated by combinations of polynomial functions. Due to the non-linearity, caused by the motor’s curve and the use of two regulators, the PID tuning algorithm has been created. The algorithm provides satisfying results, followed by a marginal difference between the requested safe distance and actual distance value. The Active Cruise Control system has been tested using normalized driving cycles, which simulate the real behaviour of a car. The simulation results prove double-PID-regulated ACC’s accuracy and speed of response in different states of motion. Full article

As a core application of V2X technology, vehicle–road collaboration enables dynamic coordination among road users (pedestrians, vehicles), infrastructure, and networks through real-time, omnidirectional information exchange. This system represents a pivotal solution for addressing critical transportation challenges, including traffic congestion, safety risks, and environmental sustainability. Its experimental teaching, as the core linkage of theoretical innovation and technical verification, is of vital importance to the cultivation of intelligent transportation talents. Compared with traditional experimental teaching, the V2X semi-physical simulation platform effectively reduces capital investment, completely eliminates the safety risks of actual road tests, and emulates the real traffic environment. To verify the teaching effectiveness of this platform, based on the OBE concept and the BOPPPS teaching method, this study constructed an experimental curriculum framework driven by learning goals and conducted an empirical analysis taking global path planning as an example. Teaching evaluation adopts a combination of subjective and objective methods: Subjective evaluation is conducted through questionnaire surveys, and the proportion of those satisfied with the teaching effect reached more than 80%. The objective evaluation consists of eight performance indicators before class, during class and after class. Through reliability analysis, the performance of students in the observation group was shown to increase by 17.39% compared with that in the control group. The results show that the experimental teaching mode based on the V2X semi-physical simulation platform significantly improves the teaching effectiveness of the vehicle–road collaboration course compared with traditional methods. Full article

This paper proposes a model identification method based on the auxiliary variable closed-loop subspace identification algorithm to address the problem of modeling difficulties caused by various complex factors affecting permanent magnet brushless DC motors in practical working conditions. This method breaks through the limitations caused by the correlation between input signals and noise in traditional subspace identification algorithms. By introducing auxiliary variables, it effectively avoids the projection process, simplifies the complex calculations of principal component analysis, and improves the practicality and efficiency of the algorithm. When constructing a data-driven identification model, the actual situation of measurement data being contaminated by noise has to be fully considered. Orthogonal compensation matrices and auxiliary variables were used to construct uncorrelated terms for noise, thereby eliminating the negative impact of noise on the model’s identification accuracy. The effectiveness of the proposed identification algorithm was verified by collecting data through a chassis dynamometer simulation test of a vehicle-mounted permanent magnet brushless DC motor. The results show that compared with the traditional N4SID algorithm, the proposed closed-loop subspace identification algorithm based on auxiliary variable principal component analysis exhibits higher model identification accuracy, stronger anti-interference ability, and better stability in both noise-free and noise-contaminated conditions, providing a more reliable model basis for motor performance evaluation and control strategy design. Full article

Automatic reverse parking (ARP) faces challenges in finding ideal reference trajectories that avoid collisions, maintain smoothness, and minimize path length. To address this, we propose an improved directional mutation moth–flame optimization algorithm with gene modification (IDMMFO-GM). We develop a practical reference trajectory optimization model by combining cubic spline interpolation with a standardized parking plane coordinate system. To effectively address the infeasible solutions encountered when parking in a garage, we apply gene modification for collision avoidance and berthing tilt generated from the reference trajectory optimization to enhance the preservation of optimization information. Furthermore, we introduce a non-linear decreasing weight coefficient and a directional mutation strategy into the moth–flame optimization algorithm to significantly improve its overall optimization performance. Taking the automatic parking garage space No. 155 in Dalian Shell Museum as the actual vehicle test object, which is situated within Dalian Xinghai Square, test results demonstrate that the proposed algorithm achieves an accelerated optimization speed, enhanced precision in trajectory optimization, and superior tracking control performance. Full article

This article proposes an innovative energy management strategy for hybrid multi-source dump trucks operating under real slope conditions in mining areas. Although previous studies have addressed the energy management issues of hybrid vehicles, few studies have taken into account complex environmental factors such as slopes under actual working conditions. The article overcomes this limitation by integrating a radial basis function (RBF) neural network to directly and accurately predict future vehicle demand power, thereby optimizing the DP-MPC strategy and improving energy efficiency. The results indicate that, compared with the traditional MPC strategy, the proposed strategy reduces fuel consumption by 3.34% and engine start-stop events by 76.2%. Additionally, when compared with another strategy that uses historical data to predict future speed and slope, calculates the vehicle’s future power demand, and incorporates it into the DP-MPC algorithm, the proposed strategy achieves comparable fuel consumption while also reducing engine start-stop events by 69.7%. Notably, the average calculation time for each step is 43.85 ms, which is substantially less than the sampling time of 1 s. To further confirm the real-time performance of the strategy, a hardware-in-the-loop (HIL) test is conducted. Full article

Wind turbines’ volume and power generation capacity have increased worldwide. Consequently, their inspection, maintenance, and repair are garnering increasing attention. Structural defects are common in turbine blades, but their detection is difficult due to the relatively large size of the blades. Therefore, engineers often use nondestructive testing. This study employed an unmanned aerial vehicle (UAV) to simultaneously capture visible-light and infrared thermal images of wind power blades. Subsequently, instant neural graphic primitives and neural radiance fields were used to reconstruct the visible-light image in three dimensions (3D) and generate a 3D mesh model. Experiments determined that after converting parts of the orthographic-view images to elevation- and depression-angle images, the success rate of camera attitude calculation increased from 85.6% to 97.4%. For defect measurement, the system first filters out the perspective images that account for 6–12% of the thermal image foreground area, thereby excluding most perspective images that are difficult to analyze. Based on the thermal image data of wind power generation blades, the blade was considered to be in a normal state when the full range, average value, and standard deviation of the relative temperature grayscale value in the foreground area were within their normal ranges. Otherwise, it was classified as abnormal. A heat accumulation percentage map was established from the perspective image of the abnormal state, and defect detection was based on the occurrence of local minima. When a defect was observed in the thermal image, the previously reconstructed 3D image was switched to the corresponding viewing angle to confirm the actual location of the defect on the blade. Thus, the proposed 3D image reconstruction process and thermal image quality analysis method are effective for the long-term monitoring of wind turbine blade quality. Full article

The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller, which is used for minimizing the route-tracking error. On the other hand, the I controller parameters and the values of the DC/DC buck converters’ components are determined by Optimizer II to minimize the voltage-tracking errors of the converters. Both optimizers work together in the system and try to minimize tracking errors while also minimizing power consumption by using suitable objective functions. Simulation results demonstrate the effectiveness of the PSO- and GWO-based design of the controllers and converters in enhancing energy efficiency and improving the quadrotor’s flight stability. For step inputs, the GWO-based optimized system shows better performance according to power consumption and the time domain criteria such as rise time and settling time. However, the PSO-based optimized system shows 24.707% better performance for overshoot. On the other hand, 10.8866% less power consumption is observed for the GWO-based optimized system. This power efficient performance of the GWO-based system increases to 18% for the complex route involving ramp and step inputs. Then, a 39 s route test was performed and the total power consumptions for the GWO-based optimized and PSO-based optimized systems were observed to be 168.0015 W/s and 179.9070 W/s, respectively. This means that GWO-based optimizers provide more energy-efficient performance for complex routes. On the other hand, it was determined that the tracking errors in the performance of the desired and actual values of both translational and rotational movement parameters and the forces and torques required for the quadrotor to follow this route were obtained at a maximum of 4% for systems optimized with both techniques. This shows that the full systems optimized with both GWO and PSO algorithms significantly increase their energy efficiency and provide maximum route-following performance. Full article

The motor drive system is pivotal for vehicles, particularly in new energy applications. However, conventional hybrid systems, which combine generator sets and single batteries in parallel configurations, fail to meet the operational demands of large pure electric mining dump trucks under fluctuating power requirements—such as high reserve power during acceleration and robust energy recovery during braking. Traditional single-motor configurations struggle to balance low-speed, high-torque operations and high-speed driving within cost-effective ranges, often necessitating oversized motors or multi-gear transmissions. To address these challenges, this paper proposes a dual-motor symmetric powertrain configuration with a seven-speed gearbox, tailored to the extreme operating conditions of mining environments. By integrating a high-speed, low-torque motor and a low-speed, high-torque motor through dynamic power coupling, the system optimizes energy utilization while ensuring sufficient driving force. The simulation results under extreme conditions (e.g., 33% gradient climbs and heavy-load downhill braking) demonstrate that the proposed configuration achieves a peak torque of 267 kNm (200% improvement over single-motor systems) and a system efficiency of 92.4% (vs. 41.7% for diesel counterparts). Additionally, energy recovery efficiency reaches 85%, reducing energy consumption to 4.75 kWh/km (83% lower than diesel trucks) and life cycle costs by 38% (USD 5.34/km). Field tests in open-pit mines validate the reliability of the design, with less than a 1.5% deviation in simulated versus actual performance. The modular architecture supports scalability for 60–400-ton mining trucks, offering a replicable solution for zero-emission mining operations in high-altitude regions, such as Tibet’s lithium mines, and advancing global efforts toward carbon neutrality. Full article