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September 10, 2025

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

MWM. M. WangBeijing Institute of Technology FZFujun ZhangBeijing Institute of Technology HLHang LvDalian University of Technology

Key Points

The proposed method enhances accuracy in road resistance coefficient estimation, achieving 92.9% accuracy on flat terrain and 94.3% on slopes.
Utilizing FFRLS and EKF, the combined approach allows for real-time identification of resistance coefficients for tracked vehicles.
Standard deviations from both methods help define confidence levels for more accurate coefficient fusion estimation.
This method signifies a substantial improvement in the precision of estimating coefficients critical for vehicle performance on unpaved roads.

Abstract

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.

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