Development and Experimental Validation of a PVDF-Based Intelligent Tire System for Agricultural Tire Load Estimation

Accurate tire load information is crucial for improving agricultural vehicle traction, mobility, and soil protection, yet real-time monitoring is difficult due to nonlinear tire deformation under varying conditions. This study developed an intelligent tire sensing system using PVDF sensors and conducted bench tests on a 280/85 R24 agricultural radial tire under different loads, inflation pressures, and speeds. Based on qualitative waveform observations, the sidewall-mounted sensor was selected for subsequent feature analysis and load-estimation modeling. Four load-sensitive features were extracted and used to train a GA-BP neural network for tire load estimation. Under the sample-level validation protocol used in this study, the GA-BP model achieved an RMSE of 34.454 N, a correlation coefficient of 99.985%, and a maximum prediction error below 1.2%. These results demonstrate the feasibility of the proposed PVDF-based tire load estimation framework under the represented experimental conditions, while independent grouped validation is required to assess its generalization to unseen operating conditions. The findings demonstrate that PVDF sensors provide an accurate and efficient method for tire load estimation in intelligent agricultural vehicles.