Data-driven Traction Characteristics Modeling and Prediction of Agricultural Tractor Transmission Systems Using Physics-informed Neural Network Approach

Agricultural tractor traction performance prediction is crucial for reducing fiel consumption and improving operation quality in modern agriculture. To address the limitations of traditional empirical modeling and regression methods in predicting tractor traction performance, where model accuracy and computational efficiency are often constrained by multidimensional nonlinear characteristics, this study introduces a Physics-informed Neural Network (PINN) framework. The proposed approach integrates the nonlinear approximation capability of neural networks with prior knowledge from traction dynamics, and incorporates physics-based constraint terms into the loss function to enhance physical consistency and generalization. Unlike conventional black-box data-driven models, the PINN framework simultaneously fits observed data and enforces consistency with physical laws during training, which prevents physically implausible predictions. Experimental results show that the PINN achieves high accuracy, particularly in predicting traction power and fuel consumption rate, with an average coefficient of determination (R2) above 95% across four output indicators. In addition, the predicted trends are highly consistent with measured data, demonstrating the potential of this method to provide strong technical support for the optimization and design of agricultural machinery power systems.