Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems

This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested real-time methodology is based on the explicit correlation between road roughness and suspension system. Because the road roughness input influences the suspension system, AEKF-UI with a forgetting factor is proposed to simultaneously estimate the time-varying parameter (vehicle mass) of vehicle suspension systems and road roughness using an unknown input estimator. However, a constant forgetting factor does not adaptively weigh the covariance of all the states, and optimal filtering cannot be ensured. To resolve this problem, we present an adaptive forgetting factor technique employed to track time-varying parameters and unknown inputs. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate the vehicle mass variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm were verified through laboratory-level experiments.

Automated summary

This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input, which simultaneously estimates the time-varying parameter of vehicle suspension systems and road roughness. An adaptive forgetting factor technique is employed to track time-varying parameters and unknown inputs.