Search Results (84)

For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems. Full article

Wheel-legged robots combine the advantages of both wheeled robots and traditional quadruped robots, enhancing terrain adaptability but posing higher demands on the perception of foot–ground contact forces. However, existing approaches still suffer from limited accuracy in estimating contact positions and three-dimensional contact forces when dealing with flexible tire–ground interactions. To address this challenge, this study proposes a foot–ground contact state detection technique and optimization method based on multi-sensor fusion and intelligent modeling for wheel-legged robots. First, finite element analysis (FEA) is used to simulate strain distribution under various contact conditions. Combined with global sensitivity analysis (GSA), the optimal placement of PVDF sensors is determined and experimentally validated. Subsequently, under dynamic gait conditions, data collected from the PVDF sensor array are used to predict three-dimensional contact forces through Gaussian process regression (GPR) and artificial neural network (ANN) models. A custom experimental platform is developed to replicate variable gait frequencies and collect dynamic contact data for validation. The results demonstrate that both GPR and ANN models achieve high accuracy in predicting dynamic 3D contact forces, with normalized root mean square error (NRMSE) as low as 8.04%. The models exhibit reliable repeatability and generalization to novel inputs, providing robust technical support for stable contact perception and motion decision-making in complex environments. Full article

Improper car tire pressure affects dynamics, fuel economy, and driver safety. Current central tire inflation systems (CTISs) regulate tire pressure relative to its reference value. However, the current CTIS is limited in its automation, as the system requires the loading of present conditions and the manual input of terrain conditions. Therefore, the system lacks intelligent components which would increase its efficiency. Adding a terrain recognition feature to the current CTIS technology, the tire pressure management system (TPMS) described in this paper enhances the capability to adjust to the ideal tire pressure according to the terrain condition. In this study, we integrate a terrain recognition component which uses a convolutional neural network (CNN), specifically, ResNet-18, into the TPMS to classify and detect terrain conditions and apply the correct pressure level. A one-tire terrain-based TPMS model was developed through system integration. The system was tested under flat, uneven, and soft terrain conditions. The CNN model demonstrated 95% accuracy in classifying the chosen terrains, with demonstrated adaptability to nighttime environments. Inflation and deflation tests were conducted at varying speeds and terrains, and the results showed longer inflation times at higher pressure ranges, while deflation times remained consistent regardless of pressure range. A negligible impact on inflation and deflation speed was observed at speeds below 15 km/h. Instantaneous response time between the microcontrollers increases efficiency in the overall CTIS process. Full article

To address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking control method based on a dilated convolutional neural network (DCNN). First, an effective dataset was constructed by incorporating historical state information, such as longitudinal tire forces and vehicle speed, to accurately capture vehicle dynamic characteristics and reflect energy variations during motion. Next, a dilated convolutional vehicle system model (DCVSM) was designed by combining vehicle dynamics with data-driven modeling techniques. This model was then integrated into a model predictive control (MPC) framework. By solving a nonlinear optimization problem, a dilated convolutional model predictive controller (DCMPC) was developed to enhance tracking accuracy and reduce energy consumption. Finally, a co-simulation environment based on CarSim and Simulink was used to evaluate the proposed method. Comparative analyses with a traditional MPC and a neural network-based MPC (NNMPC) demonstrated that the DCMPC consistently exhibited superior trajectory tracking performance under various test scenarios. Furthermore, by computing the tire-slip energy loss rate, the proposed method was shown to offer significant advantages in improving energy efficiency. Full article

To address the performance degradation in model predictive control (MPC) under vehicle state uncertainties caused by external disturbances (e.g., crosswinds and tire cornering stiffness variations) and rigid constraint conflicts, we propose a robust MPC framework with adaptive weight adjustment and dynamic constraint relaxation. Traditional MPC methods often suffer from infeasibility or deteriorated tracking accuracies when handling model mismatches and disturbances. To overcome these limitations, three key innovations are introduced: a three-degree-of-freedom vehicle dynamic model integrated with recursive least squares-based online estimation of tire slip stiffness for real-time lateral force compensation; an adaptive weight adjustment mechanism that dynamically balances control energy consumption and tracking accuracy by tuning cost function weights based on real-time state errors; and a dynamic constraint relaxation strategy using slack variables with variable penalty terms to resolve infeasibility while suppressing excessive constraint violations. The proposed method is validated via ROS (noetic)–MATLAB2023 co-simulations under crosswind disturbances (0–3 m/s) and varying road conditions. The results show that the improved algorithm achieves a 13% faster response time (5.2 s vs. 6 s control cycles), a 15% higher minimum speed during cornering (2.98 m/s vs. 2.51 m/s), a 32% narrower lateral velocity fluctuation range ([−0.11, 0.22] m/s vs. [−0.19, 0.22] m/s), and reduced yaw rate oscillations ([−1.8, 2.8] rad/s vs. [−2.8, 2.5] rad/s) compared with a traditional fixed-weight MPC algorithm. These improvements lead to significant enhancements in trajectory tracking accuracy, dynamic response, and disturbance rejection, ensuring both safety and efficiency in autonomous vehicle control under complex uncertainties. The framework provides a practical solution for real-time applications in intelligent transportation systems. Full article

Incorrect vehicle tire pressure affects vehicle dynamics, fuel efficiency, and driver safety across different terrain conditions. The current central tire inflation system (CTIS) alleviates this issue by adjusting the tire pressure to a predetermined reference level. However, the existing CTIS only adjusts the pressure based on load conditions through manual input for terrain types and lacks advanced intelligence for optimal automation. Integrating the recognition result of terrain conditions enables real-time adjustments of tire pressure and enhances driving performance and efficiency. This study aims to integrate the terrain recognition component using a convolutional neural network (CNN) by reviewing previous terrain-detection models. The CTIS was enhanced to classify and detect terrain conditions and apply the correct tire pressure level. We employed a systematic literature review (SLR) to assess the development procedures for integrating the intelligent component with the basic CTIS. ResNet-18 was used as the most appropriate CNN model to classify the terrain conditions on a gathered local dataset. A single-wheel testbed using the enhanced CTIS is appropriate for laboratory testing and system integration tests. Full article

To enhance the precision and real-time performance of trajectory tracking control in differential-steering intelligent sweeping robots and to improve the adaptability of the control algorithm to errors caused by sensor noise, tire slip, and skid, an MPC-PID (Model Predictive Control–Proportional-Integral-Derivative) dual closed-loop control strategy was proposed. This strategy integrates a Kalman filter-based state estimator and a sliding compensation module. Based on the kinematic model of the intelligent sweeping robot, a model predictive controller (MPC) was designed to regulate the vehicle’s pose, while a PID controller was used to adjust the longitudinal speed, forming a dual closed-loop control algorithm. A Kalman filter was employed for state estimation, and a sliding compensation module was introduced to mitigate wheel slip and lateral drift, thereby improving the stability of the control system. Simulation results demonstrated that, compared to traditional MPC control, the maximum lateral deviation, maximum heading angle deviation, and speed response time were reduced by 50.83%, 53.65%, and 7.10%, respectively, during sweeping operations. In normal driving conditions, these parameters were improved by 41.58%, 45.54%, and 24.17%, respectively. Experimental validation on an intelligent sweeper platform demonstrates that the proposed algorithm achieves a 16.48% reduction in maximum lateral deviation and 9.52% faster speed response time compared to traditional MPC, effectively validating its enhanced tracking effectiveness in intelligent cleaning operations. Full article

The development of intelligent tire technology has attracted increasing attention from researchers to build different tire models to obtain the state parameters of the tire and to try to correlate these parameters with sensors. To address the challenge of characterizing the evolution of wear in traditional tire mechanics models, this study proposes a physical model that incorporates tire wear. The model is an improvement over the traditional flexible ring model, incorporating brush theory. By establishing the mechanical equilibrium equation of the tread unit, a tire dynamic equation incorporating wear state variables is constructed. The strain–displacement relationship is analyzed to determine the correlation between the strain field and the displacement field. The results show that the strain signals obtained from the physical model and the finite element model maintain a high degree of consistency, validating the reliability and effectiveness of the proposed model. In addition, the correlation between the tire wear and strain signal characteristics was successfully revealed by comparing the physical model and the finite element model. The proposed model provides a theoretical foundation for future research on intelligent tires, as well as a basis for related studies on tire wear, tire lifespan, and tire mechanical properties. Full article

Intelligent tire technology significantly enhances vehicle performance and driving safety by integrating sensors and electronics within the tire to facilitate the real-time monitoring of tire–road interactions. However, its testing and validation face challenges due to the absence of integrated bench and road testing frameworks. This paper introduces a novel, comprehensive testing system designed to support the full lifecycle development of intelligent tire technologies across both laboratory and real-world driving scenarios, focusing on accelerometer and strain-based sensing. Featuring a modular, distributed architecture, the system integrates an instrumented wheel equipped with multiple embedded tire sensors and a wheel force transducer (WFT), as well as vehicle motion and driving behavior sensors. A robust data acquisition platform based on NI CompactRIO supports multiple-channel high-precision sensing, with sampling rates of up to 50 kHz. The system ensures that data performance aligns with diverse intelligent tire sensing principles, supports a wide range of test parameters, and meets the distinct needs of each development stage. The testing system was applied and validated in a tire vertical load estimation study, which systematically explored and validated estimation methods using multiple accelerometers and PVDF sensors, compared sensor characteristics and estimation performance under different installation positions and sensor types, and culminated in a product-level assessment in road conditions. The experimental results confirmed the higher accuracy of accelerometers in vertical load estimation, validated the developed estimation algorithms and the intelligent tire product, and demonstrated the functionality and performance of the testing system. This work provides a versatile and reliable platform for advancing intelligent tire technologies, supporting both future research and industrial applications. Full article

To address the challenges of unclear thermo-mechanical coupling mechanisms and unpredictable multi-field synergistic effects in segmented tire molds during vulcanization, this study focuses on segmented tire molds and proposes a multi-physics coupling numerical model. This model integrates fluid flow dynamics into heat transfer mechanisms. It systematically reveals molds’ heat transfer characteristics, stress distribution and deformation behavior under combined high-temperature and mechanical loading. Based on a fluid-solid-thermal coupling framework and experimental validations, simulations indicate that the internal temperature field of the mold is highly uniform. The global temperature difference is less than 0.13%. The temperature load has a significant dominant effect on the deformation of key components such as the guide ring and installation ring. Molding forces play a secondary role in total stress. The error between multi-field coupling simulation results and experimental results is controlled within 6%, verifying the model’s reliability. This research not only provides a universally applicable multi-field coupling analysis method for complex mold design but also highlights the critical role of temperature fields in stress distribution and deformation analysis. This lays a theoretical foundation for the intelligent design and process optimization of high-temperature, high-pressure forming equipment. Full article

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles. Full article

Tire force is a critical state parameter for vehicle dynamics control systems during vehicle operation. Compared with tire force estimation methods relying on vehicle dynamics or tire models, intelligent tire technology can provide real-time feedback regarding tire–road interactions to the vehicle control system. To address the demand for accurate tire force prediction in active safety control systems under various operating conditions, this paper proposes an intelligent tire force estimation method, integrating sensor-measured dynamic response parameters and machine learning techniques. A 205/55 R16 radial tire was selected as the research object, and a finite element model was established using the parameterized modeling approach with the ABAQUS finite element simulation software. The validity of the finite element model was verified through indoor static contact and stiffness tests. To investigate the sensitive response areas and variables associated with tire force, the ground deformation area of the inner liner was refined along the transverse and circumferential directions. Variance-based global sensitivity analysis combined with dimensional reduction methods was used to evaluate the sensitivity of acceleration, strain, and displacement responses to variations in longitudinal and lateral forces. Based on the results of the global sensitivity analysis, the influence of longitudinal and lateral forces on sensitive response variables in their respective sensitive response areas was examined, and characteristic values of the corresponding response signal curves were analyzed and extracted. Three intelligent tire force estimation models with different sensor-targeting configurations were established using radial basis function (RBF) neural networks. The mean relative error (MRE) of intelligent tire force estimation for these models remained within 10%, with Model 3 demonstrating an MRE of less than 2% and estimation errors of 1.42% and 1.10% for longitudinal and lateral forces, respectively, indicating strong generalization performance. The results show that tire forces exhibit high sensitivity to acceleration and displacement responses in the crown and sidewall areas, providing methodological guidance for the targeted sensor configuration in intelligent tires. The intelligent tire force estimation method based on the RBF neural network effectively achieves accurate estimation, laying a theoretical foundation for the advancement of vehicle intelligence and technological innovation. Full article

The tire grounding parameters are a crucial component of the vehicle dynamics control system; accurate acquisition of grounding parameters is important for improving traction, braking force, and handling stability during vehicle operation. This paper studies strain-based intelligent tire contact patch length and vertical force estimation; first, a 205/55R16 radial tire was established, and static grounding experiments were carried out to verify the validity of the finite element model. Second, the sensitivity of the circumferential strain signal of the inner liner in the contact area of a tire with complex tread patterns was discussed. Methods for estimating the contact angle and contact patch length of rolling tires were established, and the estimation accuracy under different tire parameters and operating conditions were analyzed. Finally, the vertical force-sensitive response characteristics were analyzed and extracted, and the vertical force prediction model of a radial tire based on particle swarm optimization BP neural network was established. Full article

This paper introduces a new concept of a smart and sustainable tire based on a removable tread band: ECOTIRE. Current tires, though crucial for road information and vehicle control, such as braking, traction, and turning, remain disconnected from Advanced Driver Assistance Systems (ADAS). Additionally, their production, use, and recycling pose significant environmental challenges, requiring sustainable materials and lifecycle improvements. The ECOTIRE concept makes it possible to separate the part of the tire subject to wear and apply new materials with reduced environmental impact. At the same time, the service life of the casing is extended, facilitating the introduction of sensors that improve vehicle safety. This study explores the purely mechanical connection between the casing and tread, demonstrating the feasibility of this innovative tire structure while eliminating the need for rubber matrix-based materials for a proper bond between the two components. Experimental tests using a rubber sample to simulate the tire–road contact patch validate the effectiveness of the mechanical link under varying normal loads. Grip test results, measuring longitudinal and lateral forces, show promising performance. This advancement in tire technology marks a first step toward sustainability, tire performance, and smart integration, ultimately reducing environmental impact. Full article

Accurate vertical load measurement through intelligent tire technology is crucial for vehicle stability, handling, and safety. Existing studies have mainly focused on modeling and bench experiments, overlooking a detailed comparative analysis of real sensor performance and validation under actual driving conditions. This study addresses this gap by performing sensor comparisons and extensive real-road validation to ensure the accuracy and reliability of the proposed methods. First, finite element modeling (FEM) is used to assess the feasibility of accelerometer and strain-based sensors for vertical load prediction. High-precision bench tests quantitatively compare the performance of multiple triaxial Integrated Electronics Piezoelectric (IEPE) accelerometers and Polyvinylidene Fluoride (PVDF) sensors, identifying accelerometers as the superior choice due to their better stability and linearity. Vertical load prediction algorithms are developed using Support Vector Machine (SVM) and linear regression, considering variables like contact length, vehicle speed, and tire pressure. The algorithms are validated under real-road conditions using high-performance instruments across constant speed, acceleration, braking, and cornering, and a self-designed compact Intelligent Tire Test Unit (ITTU) is deployed for product-level implementation, confirming its effectiveness in real-world driving scenarios. The findings provide a validated framework for accurate vertical load estimation and real-time tire parameter prediction, offering practical insights for improving intelligent tire technology in dynamic driving conditions. Full article