Road Friction Coefficient and Tire Cornering Characteristics Parameters Estimation Based on PMSM Sensorless Control

doi:10.3901/JME.260278

Abstract

Abstract: Aiming at the insufficient estimation accuracy of road adhesion coefficient and tire cornering characteristics parameters for distributed drive electric vehicle(DDEV), the road adhesion coefficient and tire cornering characteristics parameters estimation are constructed by combining multi-sensor fusion and physical constraint neural network modeling theories. First, the maximum correntropy(MC) criterion is integrated with square-root cubature quadrature Kalman filter(SCQKF) to construct the MC-SCQKF algorithm, where the measured covariance matrix is optimized and quadrature cubature sampling is employed to achieve accurate estimation of PMSM speed and rotor position under non-Gaussian noise, simultaneously enhancing the robustness of the system and the convergence of state estimation. Second, enabling real-time estimation of yaw rate, sideslip angle, longitudinal speed, and road friction coefficient through MC-SCQKF, and they are utilized as features to develop the physics-informed neural network for tire lateral force prediction(TirePINN) model. Finally, the cornering stiffness of front and rear tires is fitted using predicted lateral forces and calculated slip angles by the estimated vehicle state parameters, forming a self-validating closed-loop observation system for tire cornering characteristics. Hardware-in-the-loop tests show fitted errors of 1.49%(front) and 1.37%(rear) for tire cornering stiffness.

Key words: distributed drive electric vehicle, square-root cubature quadrature Kalman filter, physics-informed neural network, road friction coefficient, tire cornering characteristic parameters

CLC Number:

U461

LI Haoran, ZHOU Haichao, WANG Guolin, ZHANG Rongyun, ZHAO Chunlai. Road Friction Coefficient and Tire Cornering Characteristics Parameters Estimation Based on PMSM Sensorless Control[J]. Journal of Mechanical Engineering, 2026, 62(8): 196-209.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260278

http://www.cjmenet.com.cn/EN/Y2026/V62/I8/196

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