Search Results (1,575)

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this paper presents a novel intelligent electric drive wheel (i-EDW) with an integrated transmission system and a load-sensing unit (LSU). The i-EDW adopts an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), while the integrated LSU ensures high-precision measurement of six-dimensional wheel forces and moments. According to this multi-axis force information, a real-time estimation and stability control method based on the tire–road friction circle concept is proposed. Instead of the complex decoupling and multi-objective optimization with the multi-actuator systems, this paper focuses on minimizing the tire load rate of i-EDWs, which significantly advances the state of the art in terms of calculation efficiency and respond speed. To validate this theoretical framework, a full-vehicle model equipped with four i-EDWs is developed. In the MATLAB R2022A/Simulink co-simulation environment, a virtual prototype is tested under typical driving scenarios, including the straight-line acceleration and double-moving-lane (DML) steering. The simulation results prove a reliable safety margin from the friction circle boundaries, laying a solid foundation for precise motion control and improved system robustness in future intelligent vehicles. Full article

A new type of transportation vehicle, the maglev car, is gaining attention in the automotive and maglev industries due to its potential to meet personalized urban mobility and future travel needs. To optimize the chassis layout of maglev cars, this paper proposes a compact powertrain integrating electrodynamic suspension with in-wheel motor technology, in which a permanent magnet electrodynamic in-wheel motor (PMEIM) enables integrated propulsion and levitation. First, the PMEIM external magnetic field distribution is characterized by analytical and finite element (FEM) approaches, revealing the magnetic field distortion of the contactless powertrain. Subsequently, the steady-state electromagnetic force is modeled and the operating states of the PMEIM powertrain are calculated and determined. Next, the PMEIM electromagnetic design is conducted, and its electromagnetic structure rationality is verified through magnetic circuit and parametric analysis. Finally, an equivalent prototype is constructed, and the non-contact electromagnetic forces of the PMESM are measured in bench testing. Results indicate that the PMEIM powertrain performs propulsion and levitation functions, demonstrating 14.2 N propulsion force and 45.8 N levitation force under the rated condition, with a levitation–weight ratio of 2.52, which hold promise as a compact and flexible drivetrain solution for maglev cars. Full article

In this paper, a hierarchical adaptive control strategy is proposed to enhance the handling stability of distributed drive electric vehicles. In this strategy, the upper-level fuzzy controller calculates the additional yaw moment and rear wheel angle by utilizing the error between the actual and the target yaw velocity, as well as the error between the actual and the target sideslip angle. The quadratic programming algorithm is adopted to achieve the optimal torque distribution scheme through the lower-level controller, and the electronic stability control system (ESC) is utilized to generate the braking force required for each wheel. The four-wheel steering controller optimizes the rear wheel angle by using proportional feedforward combined with fuzzy feedback or Akerman steering based on the steering wheel angle and vehicle speed, through actuators such as active front-wheel steering (AFS) and active rear-wheel steering (ARS), which generate the steering angle of each wheel. This approach is validated through simulations under serpentine and double-lane-change conditions. Compared to uncontrolled and single-control strategies, the actuators are decoupled, the actual sideslip angle and yaw velocity of the vehicle can effectively track the target value, the actual response is highly consistent with the expected response, the goodness of fit exceeds 90%, peak-to-peak deviation with a small tracking error. Full article

This paper presents an experimental study validating the feasibility of Radio Frequency Identification (RFID) marker systems as a complementary solution for autonomous vehicle (AV) localization in Global Navigation Satellite System (GNSS)-degraded urban environments. A novel synchronized dynamic testbed featuring hardware-level integration with wheel revolution tracking enables precise correlation of RFID marker reads with vehicle angular position. Experimental results demonstrate that multi-antenna configurations achieve consistently high read success rates (up to 99.6% at 0.5 m distance), sub-meter localization accuracy (~55 cm marker spacing), and reliable performance at average urban speeds (36 km/h simulated velocity). Spatial diversity from four strategically positioned antennas overcomes multipath interference and orientation challenges inherent to high-speed RFID reading. Processing latency remains well within the 58 ms time budget critical for autonomous navigation. These findings validate RFID’s potential for smart road infrastructure integration and demonstrate a scalable, cost-effective solution for enhancing AV safety and decision-making capabilities through contextual information transmission. Full article

Vulcanized NR-SBR is widely used in vehicle components; however, its irreversible crosslinking limits recyclability and contributes to the large number of tires discarded annually worldwide, and in this context, this work presents an experimental comparative assessment of the tribological behavior of conventional tire rubber and silicone VMQ, motivated by a wheel concept based on a detachable tread aimed at improving durability and sustainability rather than proposing an immediate material substitution. Wear and friction behavior were investigated under abrasive and self-friction conditions using pin-on-disk testing with an abrasive counterpart representative of asphalt, supported by optical and scanning electron microscopy. The results show that NR-SBR undergoes severe abrasive and erosive wear, characterized by deep and irregular wear tracks, pronounced fluctuations in the dynamic friction coefficient, and strong sensitivity to load and sliding speed, particularly during the initial stages of track formation. In contrast, VMQ exhibits mild abrasive wear dominated by viscoelastic deformation, leading to shallow and stable wear tracks, lower friction coefficients, and significantly reduced material loss once the contact track is fully developed. These differences are attributed to the distinct mechanical responses of the elastomers, as the higher hardness and limited strain capacity of rubber promote micro-tearing and unstable material removal, while the high elasticity of silicone enables stress redistribution and stable contact conditions under abrasive loading. UV aging increases stiffness of rubber, resulting in reduced wear and friction, while silicone remains largely unaffected after 750 h due to the stability of its Si–O–Si backbone. Self-friction tests further indicate that smooth silicone sliding against rubber yields the lowest friction values, highlighting a favorable material pairing for detachable tread concepts. Factorial design analysis confirms material type as the dominant factor influencing both wear and friction. Overall, for the specific materials and operating conditions investigated, VMQ demonstrates higher durability, greater tribological stability, and improved aging resistance compared to NR-SBR, providing experimental evidence that supports its potential for long-life, more sustainable detachable tread applications. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

In racing cars, a low ride height is crucial for inverted wings and ground-effect systems to function effectively, significantly enhancing aerodynamic performance but also increasing sensitivity to pitch and roll variations. However, the specific impact of wheel-ground conditions on racing cars under ride-height-related attitude variations has not received attention. This study employed numerical simulations (compared with wind tunnel test data) to investigate these effects on racecar aerodynamic characteristics, analyzing three specific wheel-ground combinations: moving ground with rotating wheels (MR), moving ground with stationary wheels (MS), and stationary ground with stationary wheels (SS). A systematic analysis was conducted on aerodynamic changes associated with wheel-plane total pressure coefficient differences, upper-lower surface pressure coefficient variations, and front-rear axle aerodynamic force distributions, elucidating individual component contributions to overall performance changes induced by wheel-ground alterations. Results indicate that wheel conditions, especially rear wheels and their localized interactions with the diffuser-equipped body predominantly influence drag. In contrast, ground conditions primarily affect the body and front wing to alter downforce, with induced drag variations further amplifying total drag differences. Moreover, ground conditions’ impact on the front wing is modulated by vehicle attitude, resulting in either increased or decreased front wing downforce and thus altering aerodynamic balance. These insights highlight that ride-height related attitudes are critical variables when evaluating combined wheel-ground effects, and while wheel rotation is significant, the aerodynamic force and balance changes induced by ground conditions (as modulated by attitude) warrant greater attention. This understanding provides valuable guidance for racecar aerodynamic design. Full article

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

This paper reviews recent developments in the design optimization of electrical drive systems for electric vehicles (EVs) and proposes a pathway to develop next-generation AI design platforms that integrate system-level optimization methods and digital twins. First, a comprehensive review is presented to five design optimization models for EV motors, including multiphysics, multiobjective, multimode, robust, and topology optimization, as well as six efficient optimization strategies, such as multilevel optimization and AI-based approaches. Several recommendations on the practical application of these optimization strategies are also presented. Second, representative optimization methods for power converters and control systems of EV drives are summarized. Third, application-oriented and robust system-level design optimization strategies for EV drive systems are discussed. Finally, two proposals are presented and discussed for the design of next-generation EV drive systems and their integration with battery management systems. They are AI-powered automatic design optimization platforms that integrate large language models and a digital-twin-assisted system-level optimization framework. Two case studies on in-wheel motors and drive systems are also included to demonstrate the performance and effectiveness of various optimization methods. Full article

Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system’s operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events. Full article

The article presents the analytic method for real-time detection of the stationary state of a vehicle based on information retrieved from 6 DoF IMU sensor. Reliable detection of stillness is essential for the application of resetting the inertial sensor’s output bias, called Zero Velocity Update method. It is obvious that the signal from the strapped on inertial sensor differs while the vehicle is stationary or moving. Effort was then made to find a computational method that would automatically discriminate between both states with possibly small impact on the vehicle embedded controller. An algorithmic step-by-step method for building, optimizing, and implementing a diagnostic system that detects the vehicle’s stationary state was developed. The proposed method adopts the “Mahalanobis Distance” quantity widely used in industrial quality assurance systems. The method transforms (fuses) information from multiple diagnostic variables (including linear accelerations and angular velocities) into one scalar variable, expressing the degree of deviation in the robot’s current state from the stationary state. Then, the method was implemented and tested in the dead reckoning navigation system of an autonomous wheeled mobile robot. The method correctly classified nearly 93% of all stationary states of the robot and obtained only less than 0.3% wrong states. Full article

The hard bottom layer in paddy fields significantly impacts the driving stability, operational quality, and efficiency of agricultural machinery. Continuously improving the precision and efficiency of unmanned, precision operations for paddy field machinery is essential for realizing unmanned smart rice farms. Addressing the unclear influence patterns of hard bottom contours on typical scenarios of agricultural machinery motion and posture changes, this paper employs a rice transplanter chassis equipped with GNSS and AHRS. It proposes methods for acquiring motion state information and hard bottom contour data during agricultural operations, establishing motion state expression models for key points on the machinery antenna, bottom of the wheel, and rear axle center. A correlation analysis method between motion state and hard bottom contour parameters was established, revealing the influence mechanisms of typical hard bottom contours on machinery trajectory deviation, attitude response, and wheel trapping. Results indicate that hard bottom contour height and local roughness exert extremely significant effects on agricultural machinery heading deviation and lateral movement. Heading variation positively correlates with ridge height and negatively with wheel diameter. The constructed mathematical model for heading variation based on hard bottom contour height difference and wheel diameter achieves a coefficient of determination R² of 0.92. The roll attitude variation in agricultural machinery is primarily influenced by the terrain characteristics encountered by rear wheels. A theoretical model was developed for the offset displacement of the antenna position relative to the horizontal plane during roll motion. The accuracy of lateral deviation detection using the posture-corrected rear axle center and bottom of the wheel center improved by 40.7% and 39.0%, respectively, compared to direct measurement using the positioning antenna. During typical vehicle-trapping events, a segmented discrimination function for trapping states is developed when the terrain profile steeply declines within 5 s and roughness increases from 0.008 to 0.012. This method for analyzing how hard bottom terrain contours affect the position and attitude changes in agricultural machinery provides theoretical foundations and technical support for designing wheeled agricultural robots, path-tracking control for unmanned precision operations, and vehicle-trapping early warning systems. It holds significant importance for enhancing the intelligence and operational efficiency of paddy field machinery. Full article

In deep-sea mining operations, the seabed sediments (mud and sand) are very soft and slippery. This often causes tracked vehicles to slip and veer off course when they are driving on the seafloor. To solve the path-tracking problem for deep-sea mining vehicles, this study suggests a path-tracking controller that can adapt to the seabed environment. Firstly, it is necessary to establish a kinematic and dynamic model of the mining vehicle’s motion, analysing its seabed slippage and force application. The system has been developed on the basis of the Stanley algorithm and utilises a two-degree-of-freedom kinematic model, with lateral deviation and heading deviation acting as inputs. The establishment of fuzzy rules to adjust the gain parameter K enables the mining vehicle to adaptively modify its gain parameters according to the seabed environment and path. Secondly, a fuzzy PID controller is established and optimised to address the limitation that fuzzy PID control rules are constrained by the designer’s experience. At the same time, a relationship was established between how fast the drive wheel accelerates and the slip rate based on the dynamic model. This stops the drive wheel from slipping by limiting how fast it can go. Finally, a mechanical model of the mining vehicle was created in Recurdyn and a system model was developed in MATLAB/Simulink for joint simulation analysis. The simulation results demonstrate the efficacy of the proposed control strategy, establishing it as a reliable method for tracking the path of subsea mining vehicles. Full article

Traditional front-wheel-steering (FWS) unmanned vehicles frequently encounter maneuverability bottlenecks in confined spaces or during rapid formation changes due to inherent kinematic limitations. To mitigate these constraints, this study proposes an adaptive multi-mode (AMM) cooperative formation control framework tailored for four-wheel independent drive and steering (4WIDS) platforms. The methodology constructs a unified planner based on the virtual structure concept, integrated with an autonomous steering-mode selector. By synthesizing real-time mission requirements with longitudinal and lateral tracking errors, the system dynamically switches between crab steering, four-wheel counter-steering (4WCS), and conventional FWS modes to optimize spatial utilization. Validated within a seven-vehicle MATLAB/Simulink environment, simulation results demonstrate that the crab-steering mode significantly reduces relocation time for small lateral adjustments by eliminating redundant heading changes, whereas FWS and 4WCS modes are preferentially selected to ensure stability during high-speed or large-span maneuvers. These findings confirm that the proposed AMM strategy effectively reconciles the trade-off between agility and stability, providing a robust solution for complex cooperative maneuvering tasks. Full article