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Tire states and capacity monitoring is critical for vehicle and wheel stabilization controls in automated driving and active safety systems. Tire capacity, which represents the performance margin of tire forces from its limits, determines the operational range for vehicle control systems and their actuation through steering or torques at each tire to maintain stability while performing trajectory following. This paper presents a generic tire capacity identification framework that can handle different normal loads, road surface friction, and combined-slip driving scenarios, which are challenging for stabilization and tracking control programs in automated driving systems. A novel measuring method for generating force-training data is designed by combining the indoor tire test procedure and tread rubber friction test rig, in order to obtain adequate and high-quality benchmark datasets. The results from large data sets from road experimenting and indoor tire test facilities, including pure- and combined-slip conditions, confirm effectiveness of the developed learning-based tire capacity estimation which utilizes notions from the model description with bounded uncertainty. More importantly, the proposed method can provide reliable tire properties ranging from the linear to the sliding regions. Further validation is performed on a real test car with on-board sensory measurements, and the results confirm accuracy of the proposed method for various free rolling and hard launch/brake scenarios. [ABSTRACT FROM AUTHOR]

Understanding the dynamic behavior of tires and their interactions with roads plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about tire–road interactions through tire-embedded sensors is desirable for developing enhanced vehicle control systems. Thus, the main objectives of this research are: 1) to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles and 2) to develop a lateral force predictor based on a machine learning tool, more specifically, the Gaussian process regression (GPR) technique. It is determined that the proposed intelligent tire system can provide reliable information about the tire–road interactions even in the case of high slip angles. In addition, lateral force models based on GPR can predict forces very well, outperforming other machine learning models and providing levels of uncertainty that can be useful for designing vehicle control strategies. [ABSTRACT FROM AUTHOR]

In this paper, vehicle stability with time‐varying tire cornering stiffness and tire slip energy torque distribution is investigated for four‐in‐wheel motor drive electric vehicles thanks to the control flexibility. On the issue of tire slip energy, a scheme of "semi‐empirical tire slip energy model" is proposed and analyzed. In this scheme, the hierarchical control method is used to reduce tire slip energy and improve vehicle stability. In the outer loop controller, cost control is designed based on optimal guaranteed cost theory, which is compared with the traditional linear quadratic regulator. The integrated control method of active front steering and direct yaw moment control is evaluated and studied from the cost perspective by norm‐bounded uncertainty. Finally, the simulation results verify that the control framework considering time‐varying uncertainty has significant advantages under the condition of high‐friction coefficient single‐lane change and low‐friction coefficient slalom change. Compared with the linear quadratic regulator and the minimization of tire slip energy control, the control framework with optimal guaranteed cost can achieve smaller sideslip angle; meanwhile, it can save more tire slip energy. [ABSTRACT FROM AUTHOR]

The concept of intelligent tires has drawn the attention of researchers in the areas of autonomous driving, advanced vehicle control, and artificial intelligence. The focus of this paper is on intelligent tires and the application of machine learning techniques to tire force estimation. We present an intelligent tire system with a tri-axial acceleration sensor, which is installed onto the inner liner of the tire. Neural Network techniques are used for real-time processing of the sensor data. The accelerometer is capable of measuring the acceleration in x,y, and z directions. When the accelerometer enters the tire contact patch, it starts generating signals until it fully leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual forces are measured. Signals generated by the accelerometer and MTS Flat-Trac testing system are used for training three different machine learning techniques with the purpose of online prediction of tire forces. It is shown that the developed intelligent tire in conjunction with machine learning is effective in accurate prediction of tire forces under different driving conditions. The results presented in this work will open a new avenue of research in the area of intelligent tires, vehicle systems, and tire force estimation. [ABSTRACT FROM AUTHOR]

Tire slip angle is a vital parameter in tire/vehicle dynamics and control. This paper proposes an accurate estimation method by the fusion of intelligent tire technology and machine-learning techniques. The intelligent tire is equipped by MEMS accelerometers attached to its inner liner. First, we describe the intelligent tire system along with the implemented testing apparatus. Second, experimental results under different loading and velocity conditions are provided. Then, we show the procedure of data processing, which will be used for training five different machine learning techniques to estimate tire slip angles. The results show that the machine learning techniques, especially in frequency domain, can accurately estimate tire slip angles up to 10 degrees. More importantly, with the accurate tire slip angle estimation, all other states and parameters can be easily and precisely obtained, which is significant to vehicle advanced control, and thus this study has a high potential to obviously improve the vehicle safety especially in extreme maneuvers. [ABSTRACT FROM AUTHOR]

Energy efficiency is extremely important for electric vehicles to improve their driving range. As the motor output energy directly acts on the tire, the tire slip energy generated by tire excessive slip will reduce the effective utilization of motor output energy, resulting in the reduction of energy efficiency, especially in the case of low friction coefficient, or even vehicle instability in some changes, such as slalom change, etc. Therefore, this paper designs an integrated chassis control and verifies the energy utilization. First, based on model predictive control (MPC), an integrated chassis controller is proposed by explicitly incorporating both motor energy and tire slip energy in the main objective function. Second, the reference control actions of the torque distribution method optimized for the motor energy, which facilitates solution-search process of MPC. Then, a semi-empirical UniTire tire slip energy model is proposed for derivation of the tire dissipation energy. Acceleration and stability tests are conducted by four in-wheel independent motor-drive electric vehicles (4WIMD-EVs) on winter proving ground, respectively. The acceleration change results show that both tire dissipation energy and motor output energy are well suppressed to achieve energy-efficient, and the energy utilization ratio of tire dissipation energy to motor output energy is only 35% to achieve stable acceleration. The slalom change verifies vehicle stability, and the results show that the slip ratio of four wheels only reaches to 0.04, and the motor output energy of four wheels is effectively utilized to generate differential yaw moment. [ABSTRACT FROM AUTHOR]

Highlights • A hierarchical control framework and an adapted distribution method was proposed. • A new semi-empirical tire slip energy model with higher precision is developed. • The vehicle stability and minimizing tire slip energy are integrated in torque distribution. • The proposed controller and distribution methods saves up to 16.62% tire slip energy. Abstract Tire wear and overuse are serious issues for four-in-wheel-motor-driven electric vehicles (FIWMD EVs). As a result, this paper proposes a hierarchical control framework to improve the safety of FIWMD EVs while save the tire slip energy (i.e. reduce the tire wear), which includes a linear quadratic regulator (LQR) in the outer layer and a holistic corner controller (HCC) in the inner layer. The LQR can highly improve the lateral stability of the vehicles under extreme conditions by producing an additional yaw moment and a front wheel steering angle. Whereas, the HCC can efficiently distribute the deviation of the longitudinal force and the additional yaw moment by a step-ahead prediction. In this framework, to reduce the tire slip energy, a semi-empirical tire slip energy model is developed and used for tuning the HCC controller. In this way, not only the lateral stability of such EVs can be ensured but also the tire slip energy can be reduced. More importantly, this paper compares the lateral stability and slip energy of the vehicle under lane change condition between four methods – without control, traditional axis distribution, tire workload usage and tire slip energy. The results demonstrate that the proposed controller presents an excellent control capability. In addition, different from the widely used axis load distribution, the longitudinal velocity of the proposed method is more stable. More importantly, the tire slip power and energy are much smaller than others. It achieves 16.62% reduction of the tire slip energy. [ABSTRACT FROM AUTHOR]

This paper presents a novel nonlinear dynamic model of a multi-axle steering vehicle to estimate the lateral wear amount of tires. Firstly, a 3DOF nonlinear vehicle dynamic model is developed, including dynamic models of the hydropneumatic suspension, tire, steering system and toe angle. The tire lateral wear model is then built and integrated into the developed vehicle model. Based on the comparison of experimental and simulation results, the nonlinear model is proved to be better than a linear model for the tire wear calculation. In addition, the effects of different initial toe angles on tire wear are analyzed. As simulation results shown, the impact of the dynamic toe angle on the tire wear is significant. The tire wear amount will be much larger than that caused by normal wear if the initial toe angle increases to 1° - 1.5°. The results also suggest that the proposed nonlinear model is of great importance in the design and optimazation of vehicle parameters in order to reduce the tire wear. [ABSTRACT FROM AUTHOR]

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning (ML) algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the reference value of slip ratio can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, ML algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which creates new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation. • An intelligent tire system is presented to develop a novel slip ratio estimation method. • Important features are identified by analyzing the correlation between the accelerations and slip ratio. • Different machine learning algorithms are trained to build the slip ratio estimation models. • The proposed method can accurately estimate the slip ratio range up to ± 30% under different working conditions. [ABSTRACT FROM AUTHOR]

Intelligent tires, as an emerging technology, have great potential in real-time monitoring of tire-road contact information and new automotive active safety design. In this paper, a tire-road friction coefficient estimation method is proposed based on intelligent tire technology. First, an intelligent tire finite element model with the use of five PVDF piezoelectric film sensors attached to the inner liner of the tire is built using ABAQUS software, and the validity of the finite element model is verified by a piezoelectric intelligent tire test platform. Then, through the finite element analysis method and control variable method, the influence of sideslip angle on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed, and when the sideslip angle is 4°, the influence of load, tire pressure, vehicle speed and slip ratio on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed. Finally, based on the signal response analysis of the piezoelectric film sensors, the response mechanism of the predicted object and the linear correlation analysis, the input eigenvalues of each prediction model are extracted. The longitudinal contact patch length estimation model based on the numerical method and the sideslip angle, vertical force, lateral force and aligning moment estimation model based on the neural network algorithm are built, and the predicted parameters are input into the brush tire model to solve the tire-road friction coefficient. The result shows that the estimation error percentage of the friction coefficient estimation method without genetic algorithm optimization is 16.98%, and the estimation error percentage of the friction coefficient estimation method with genetic algorithm optimization is 5.14%, indicating that the genetic algorithm optimization effect is ideal, and the friction coefficient estimation method is practical. [Display omitted] ● Developing an intelligent tire finite element model equipped with PVDF sensors. ● Real vehicle test verifies the validity of the intelligent tire finite element model. ● Analyzing the signal response of PVDF sensors under different working conditions. ● Estimating the tire-road friction coefficient using intelligent tire system and brush tire model. [ABSTRACT FROM AUTHOR]