Joint estimation method of vehicle mass and road slope based on unscented Kalman filter

Purpose-Vehicle mass and road slope are crucial for vehicle control systems, yet they are difficult to measure in traditional vehicles during driving. This paper aims to address this challenge by proposing a joint estimation method for vehicle mass and road slope, highlighting its significance in improving vehicle control and energy efficiency. Design/methodology/approach-Based on the easy to measure driving torque and speed of distributed hub motor vehicles, this study uses the unscented Kalman filter (UKF). An estimation framework is constructed relying on the vehicle's longitudinal dynamics model. Separate estimation models for road slope and vehicle mass are established. First, the initial vehicle mass value is input to estimate the road slope dynamically. Then, the estimated slope value is fed back as input to the mass estimation model, forming a closed loop joint estimation system. Findings-Through model simulations and experiments under various operating conditions, it is verified that the joint estimation system can effectively perform parameter correction and prediction during the simultaneous estimation process of the two estimators. It achieves rapid and accurate estimations, validating the effectiveness of the proposed joint estimation algorithm. Originality/value-The originality of this work lies in the proposed closed loop joint estimation system for vehicle mass and road slope using UKF, which provides a novel solution to the long standing problem of difficult to measure parameters in traditional vehicle driving, and contributes to the development of vehicle control technology.