Search Results (6)

Accurately and quickly estimating the peak pavement adhesion coefficient is crucial for achieving high-quality driving and for optimizing vehicle stability control strategies. However, it also helps with putting forward higher requirements for vehicle driving states, tire model construction, the speed of the convergence, and the precision of the estimation algorithm. This paper unequivocally presents two highly effective methods for accurately estimating the peak pavement adhesion coefficient. Firstly, a new dimensionless tire model is constructed. A relationship between the mechanical tire characteristics and peak adhesion coefficient is established by using the Burckhardt model’s analogy between the adhesion coefficient and peak adhesion coefficient, and the UKE algorithm completes the estimation. Secondly, an adaptive variable universe fuzzy algorithm (AVUFS) is established using the follow-up of the adhesion coefficient between the tire and the road surface. Even if the slip rate is less than 5%, the algorithm can still complete accurate estimations and does not depend on the initial given information. Finally, using the estimation advantages of the two algorithms, fusion optimization is performed, and the best estimation result is obtained. Based on the simulation results, the algorithm can quickly and precisely predict the maximum pavement adhesion coefficient in situations where the pavement has a low or high adhesion coefficient. Full article

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision. Full article

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately. Full article

The tire-road peak adhesion coefficient (TRPAC), which cannot be directly measured by on-board sensors, is essential to road traffic safety. Reliable TRPAC estimation can not only serve the vehicle active safety system, but also benefit the safety of other traffic participants. In this paper, a TRPAC fusion estimation method considering model uncertainty is proposed. Based on virtual sensing theory, an image-based fusion estimator considering the uncertainty of the deep-learning model and the kinematic model is designed to realize the accurate classification of the road surface condition on which the vehicle will travel in the future. Then, a dynamics-image-based fusion estimator considering the uncertainty of visual information is proposed based on gain scheduling theory. The results of simulation and real vehicle experiments show that the proposed fusion estimation method can make full use of multisource sensor information, and has significant advantages in estimation accuracy, convergence speed and estimation robustness compared with other single-source-based estimators. Full article

To obtain the tire–pavement peak adhesion coefficient under different road states, a field measurement and FE simulation were combined to analyze the tire–pavement adhesion characteristics in this study. According to the identified texture information, the power spectral distribution of the road surface was obtained using the MATLAB Program, and a novel tire hydroplaning FE model coupled with a textured pavement model was established in ABAQUS. Experimental results show that here exists an “anti-skid noncontribution area” for the insulation and lubrication of the water film. Driving at the limit speed of 120 km/h, the critical water film thickness for the three typical asphalt pavements during hydroplaning was as follows: AC pavement, 0.56 mm; SMA pavement, 0.76 mm; OGFC pavement, 1.5 mm. The road state could be divided into four parts dry state, wet sate, lubricated state, and ponding state. Under the dry road state, when the slip rate was around 15%, the adhesion coefficient reached the peak value, i.e., around 11.5% for the wet road state. The peak adhesion coefficient for the different asphalt pavements was in the order OGFC > SMA > AC. This study can provide a theoretical reference for explaining the tire–pavement interactions and improving vehicle brake system performance. Full article

As the main branch of the brake-by-wire system, the electro-mechanical brake (EMB) system is the future direction of vehicle brake systems. In order to enhance the vehicle braking effect and improve driver safety, a convolutional neural network (CNN) online road surface identification algorithm and an optimal slip ratio tracking integral sliding mode controller (ISMC) combined EMB braking control strategy is proposed in this paper. Firstly, according to the quarter-vehicle model and Burckhardt tire model, the vehicle braking control theory based on the optimal slip ratio is analyzed. Secondly, using the VGG-16 CNN method, an online road surface identification algorithm is proposed. Through a comparative study under the same dataset conditions, it is verified that the VGG-16 method has a higher identification accuracy rate than the SVM method. In order to further improve the generalization ability of VGG-16 CNN image identification, data enhancement is performed on the road surface image data training set, including image flipping, clipping, and adjusting sensitivity. Then, combined with the EMB system model, an exponential approach law method-based ISMC is designed to achieve the optimal slip ratio tracking control of the vehicle braking process. Finally, MATLAB/Simulink software is used to verify the correctness and effectiveness of the proposed strategy and shows that the strategy of real-time identifying road surface conditions through vision can make the optimal slip ratio of vehicle braking control reasonably adjusted, so as to ensure that the adhesion coefficient of wheel braking always reaches the peak value, and finally achieves the effect of rapid braking. Full article