Tyre Slip Ratio Estimation Using Intelligent Tyre Concept

Intelligent tyres can offer crucial insights into tyre dynamics, serving as a fundamental information source for vehicle state estimation and thereby enabling vehicular safety control. Among the numerous tyre parameters, slip ratio stands out as a direct influencer of vehicle motion characteristics. Accurate estimation of tyre slip ratio is essential for vehicle safety. Firstly, an analysis of the fundamental composition of tyres was conducted, and appropriate simplifications were applied to the tyre structure. Additionally, a finite element model of the tyre was constructed using ABAQUS software. To validate the reliability of the model, a real vehicle testing system was established, consisting of the experimental vehicle, data acquisition system, and supervisory computer. The reliability of the finite element model was confirmed by assessing the consistency of acceleration signals in three different directions of the tyre. Secondly, the variations in acceleration curves under different slip ratios were examined, revealing the most prominent features linearly correlated with slip ratios within the acceleration curves. These distinctive features were then extracted as inputs. Finally, a slip ratio prediction method based on the theory of backpropagation (BP) neural networks was proposed. A neural network prediction model was constructed with five distinctive features as inputs and slip ratio as the output. This model successfully achieved the estimation of tyre slip ratios. The result shows that the MAPE of the test set is 2.32%, and the prediction accuracy is high. It also reveals the fusion of intelligent tyre technology and neural network theory has great potential in predicting tyre slip ratio.