跳到主要导航 跳到搜索 跳到主要内容

Online identification and fusion estimation of road resistance coefficient based on FFRLS and EKF

  • Beijing Institute of Technology

科研成果: 期刊稿件文章同行评审

摘要

To improve the accuracy of estimating the coefficient of road resistance for tracked vehicles on unpaved roads, this study proposes an online estimation method combining linear regression and nonlinear system analysis. The method utilizes Forgetting Factor Recursive Least Square (FFRLS) and Extended Kalman Filtering (EKF) to dynamically identify the coefficients for ground deformation resistance and slope resistance in real-time. Confidence levels are defined by calculating the standard deviations of both algorithms, and the estimation results from both methods are fused based on these confidence levels. The results demonstrate that the accuracy of this method in identifying road resistance coefficient (RRC) under flat and sloping conditions is 92.9% and 94.3%, respectively, significantly enhancing the precision of coefficient estimation.

源语言英语
页(从-至)4728-4737
页数10
期刊Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
240
7
DOI
出版状态已出版 - 6月 2026
已对外发布

指纹

探究 'Online identification and fusion estimation of road resistance coefficient based on FFRLS and EKF' 的科研主题。它们共同构成独一无二的指纹。

引用此