Joint estimation of tire cornering stiffness and vehicle operating state for distributed drive electric vehicles

Accurate vehicle state estimation and tire cornering stiffness are crucial for advanced chassis control systems. However, most existing vehicle state estimation methods rely on assumed or fixed values of tire cornering stiffness. Because of the highly nonlinear characteristics of vehicle tires and varying driving conditions, these parameters may undergo significant changes. This paper proposes a joint estimation method based on Forgetting Factor Recursive Least Squares (FFRLS) and Dung Beetle Optimizer‐Square‐root Cubature Kalman Filter (DBO‐SCKF). The FFRLS algorithm is employed to estimate tire cornering stiffness in real time, enabling adaptation to changes in tire characteristics under different conditions. Subsequently, the estimated stiffness parameters are incorporated into the DBO‐SCKF algorithm for vehicle state estimation, enhancing accuracy in dynamic driving environments by continuously updating the model parameters. Finally, simulation experiments are conducted for validation. The results demonstrate that, compared to the Cubature Kalman Filter (CKF), the proposed FFRLS‐DBO‐SCKF algorithm reduces the root mean square error (RMSE) of yaw rate, sideslip angle, and longitudinal speed by at least 65.7% in double‐lane‐change conditions. Similarly, compared to the Extended Kalman Filter (EKF), it improves estimation accuracy by at least 62.9% in serpentine conditions.