ABSTRACT

The accelerometer-based intelligent tire has gained focus in recent years for its ability to obtain both kinematics and dynamics-related information of the tire. This paper extends the previous steady-state applications of acceleration signals, which mainly estimate tire force, sideslip, and friction coefficient from the steady-state features of acceleration waveforms, to transient acceleration applications. By using the proposed tire mixed Euler–Lagrange rolling model, it is analytically demonstrated that tire rolling acceleration can be decomposed into steady-state and transient-state components from the perspective of kinematics. It is hard to analyze the transient-state component theoretically or split it from the measured signals on real road surfaces; thus, a learning-based algorithm is developed to automatically extract discriminative features without any physical models. With this method, essential information associated with tire transient acceleration could be inferred to help improve driving safety and performance.

As the application, tire wear identification with an artificial neural network is validated to be feasible based on complete acceleration signals. The prediction accuracy reaches 98.2% under different test conditions. The proposed acceleration formation mechanism is proved to be effective in explaining tire rolling acceleration as well as guiding to acquire vital information about the tire to improve vehicle safety and performance.

ABSTRACT

Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.).

This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).

ABSTRACT

Two proposed methods to determine the adhesion friction coefficient were validated by experimental results of two types of rubber compounds at different sliding velocities under dry conditions. The experimental results were measured from a linear friction tester, while the viscoelastic friction coefficient was estimated using the Persson's contact theory. Adhesive friction (model 1) was derived from the deconvolution of dry friction coefficient in two Gaussian-like curves. Interesting results were obtained using the deconvoluted method in the range of intermediate sliding velocities where preponderant contribution to the adhesion friction is replaced by the viscoelastic friction. Fitting parameter results were in good general agreement with values derived from the literature, confirming the influence of the mechanical properties of the compound and substrate texture on the proposed adhesion frictional method. The second adhesive friction model (model 2) was based on the confinement rheology of rubber chains on the contact with the asperities of the road surface. We demonstrated that acceptable adhesion friction results were achieved from a dynamic viscosity test at low frequencies, confirming the applicability of the proposed rheological model. Moreover, the relationship between the rubber composition and the modified contact layer along with the likely interphase reaction are also discussed.

ABSTRACT

In this study, the effect of a passenger car radial tire rim width on its force and moment characteristics (behavior) and subsequently on a vehicle dynamic performance is investigated. The study on the sensitivity of tire behavior to changing its rim width was carried out using the finite element method with the help of Abaqus. For this purpose, the finite element models of a 185/65R14 tire with two rim widths of 5.5″ and 6″ were created and compared in terms of steady-state longitudinal, steady-state lateral, transient longitudinal, and transient lateral behavior, by simulation of stiffness measuring and slip tests. To investigate the effect of rim width on vehicle dynamic performance, several road tests were simulated using a full vehicle dynamic model from CarSim. In each test, at least one of the parameters associated with the discussion of vehicle dynamic performance evaluation including brake distance, understeer gradient, vehicle slip angle, lateral deviation, and roll angle is measured. The vehicle model used is one of the B class instant vehicle models from CarSim, for which this tire size is appropriate. For all subsystems except tires, the same default values were used. For tires, the Pacejka 5.2 tire model is used. Numerical values of the coefficients of this tire model are calculated by fitting the curves of the tire forces and moments obtained from the finite element simulation of required tire tests. The results of the road test simulations have shown that the vehicle with the narrower rim has the better braking ability, but its other behavioral aspects are weaker.