We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Joint Estimation of Mass and Center of Gravity Position for Distributed Drive Electric Vehicles Using Dual Robust Embedded Cubature Kalman Filter.
- Authors
Zhang, Zhiguo; Yin, Guodong; Wu, Zhixin
- Abstract
The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively.
- Subjects
KALMAN filtering; ELECTRIC drives; CENTER of mass; ELECTRIC vehicles; STANDARD deviations
- Publication
Sensors (14248220), 2022, Vol 22, Issue 24, p10018
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s222410018