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

• Published in: Electronics (Basel) Vol. 11; no. 16; p. 2544
• Main Authors: Yang, Haolin, Kim, Bo-Gyu, Oh, Jong-Seok, Kim, Gi-Woo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; Algorithms; Embedded systems; Estimation theory; Kalman filtering; Kalman filters; Parameter estimation; Parameters; Roads; Roads & highways; Roughness; Sensors; South Korea; Streets; Suspension systems; Vehicles; South Korea
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11162544

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.