Robust Mass Estimation for Enhanced Braking Distance Calculation in Electric Vehicles With Autonomous Emergency Braking Systems

This study presents a preliminary investigation into robust vehicle mass estimation to enhance braking distance calculations for autonomous emergency braking systems. While active research has focused on headway distance estimation, primarily using computer vision systems such as cameras or light detection and ranging, there remains considerable room for improvement in reliable autonomous emergency braking systems. In this study, we propose a novel approach to vehicle mass estimation that leverages vehicle longitudinal dynamics, considering that vehicle mass influences braking distance as well as vehicle velocity. We employ an adaptive extended Kalman filter that combines the capabilities of the extended Kalman filter with a fading factor. This algorithm aims to estimate the time-varying vehicle mass using measurements of vehicle longitudinal velocity and driving torque inputs. Subsequently, the estimated vehicle mass serves as the basis for calculating more accurate braking distances. The proposed vehicle mass estimation algorithm is rigorously simulated using MATLAB/SIMULINK, and it undergoes an in-vehicle test.