Vehicle Mass and Road Gradient Identification by Extended Kalman Filter Based on Mass Pre‐Estimation

ABSTRACT Information on road gradient and vehicle mass is critical for the performance of the automatic shift system. This study presents a real‐time identification method for estimating vehicle mass and road gradient using an extended Kalman filter (EKF) enhanced with a mass pre‐estimation step. As a sensorless approach, it offers considerable practical advantages for heavy‐duty trucks equipped with automatic transmissions. First, a Kalman state equation is formulated based on the longitudinal vehicle dynamics model. Subsequently, an EKF‐based estimation algorithm is designed to identify both vehicle mass and road gradient. To accelerate convergence in real‐time identification, driving data collected during clutch disengagement in the gear‐shifting phase are utilized for mass pre‐estimation prior to the EKF process. This pre‐estimated mass value then serves as the initial condition for the EKF, significantly reducing the convergence time. The efficacy of the proposed method is demonstrated through truck experiments. Results show that both road gradient and vehicle mass converge to near the true values within 2 s. With mass pre‐estimation, the convergence time is reduced by approximately 60% compared to the standard EKF initialized with a random mass value. Moreover, the low RMSE confirms that the identification accuracy is sufficiently high for automatic shifting strategy applications. In conclusion, the mass pre‐estimation‐based extended Kalman filter offers an effective and rapid solution for estimating truck mass and road gradient.