Enhanced Tire–Snow Sinkage Modeling for Optimized Electric Vehicle Traction Control in Northern China Snow Conditions

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Abstract
The interaction between tires and snow layer is fundamental for vehicle safety on snowy roads. Due to the instantaneous high torque output characteristics of electric vehicles, they are more prone to slipping when driving in snow, which exacerbates the complexity of tire–snow interaction. In order to construct a more accurate tire–snow interaction model in Northern China, the Bekker formula is introduced to establish the snow pressure–sinkage relationship formula, and the parameters are calibrated by disk experiments. Then the improved tire–snow interaction model is proposed by combining the use of the brush model on the rigid road surface and the dynamic discussion of the tire’s motion behavior on the snow. A coupled finite element (FE) tire model and discrete element (DE) snow terrain model are established, with interactions governed by snow–rubber contact mechanics. The simulation tests the sinking depth of tires on snowy road surface under different slip rates and different loads, as well as the force on tires. The model provides high-precision input to the EV snow traction control algorithm to optimize motor torque distribution to improve energy efficiency. By comparing and analyzing with theoretical values, the traditional empirical model, and the modified physical model, it is finally concluded that the modified model has better reliability than the original model. Compared with the empirical model, the improved model reduces the vertical stress prediction error from 5% to less than 1%, and the motion resistance error from 6% to approximately 2%, providing high-precision input for the snow traction control of electric vehicles.
Funding Information
  • National Key Laboratory Open Fund for Automobile Chassis Integration and Bionics (20230206, 52172367, 51705220, XSJCX24_31)
  • National Natural Science Foundation of China (20230206, 52172367, 51705220, XSJCX24_31)
  • National Natural Science Foundation of China (20230206, 52172367, 51705220, XSJCX24_31)
  • Graduate Practice and Innovation Program Project of Jiangsu University of Technology (20230206, 52172367, 51705220, XSJCX24_31)

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