Physically Informed Sideslip Angle Estimation for Electric Vehicles Using Lateral Tire Force Sensors and a GPR-UKF Observer

Abstract

This article presents a nonlinear sideslip angle estimation framework that directly incorporates lateral tire force measurements into an observer structure. To address the limitations of conventional approaches that rely on tire models and slip angle approximations, a physically grounded tire model is developed that features load-dependent cornering stiffness, relaxation dynamics, and time-varying parameter adaptation. Cornering stiffness is estimated via a regression-based method using only measurable signals, and a Gaussian process regression (GPR) model is introduced to estimate the front-rear cornering stiffness. The resulting estimates are integrated into an unscented Kalman filter (UKF) observer for robust sideslip angle estimation under nonlinear and transient conditions. The framework is experimentally validated using a full-scale vehicle equipped with in-wheel motors (IWMs) and lateral tire force sensors. Results confirm that the proposed UKF-based observer achieves accurate and stable sideslip angle estimation during aggressive maneuvers and across varying road surfaces. This approach enables high-fidelity, real-time state estimation for advanced driver-assistance and automated driving applications.