Joint Estimation of Freight Vehicle Mass and Road Slope under Complex Conditions

Freight vehicle mass and road slope estimation are the keys to improving vehicle control and energy conservation. Most of the existing road slope estimation studies focus on the longitudinal slope. There is a lack of research on slope estimation in the coupling scenario of longitudinal road slopes and continuous turning uphill and downhill. Therefore, aiming at the problem of mass estimation stability in the acceleration process of freight vehicles, the influence of different acceleration types on mass estimation is explored. A joint estimation method of M-estimation and forgetting factor recursive least square (FFRLS) is proposed to realize the robust estimation of freight vehicle mass. Furthermore, the problem of road slope estimation under complex conditions is analyzed based on the mass estimation results. Considering the coupled complex conditions, such as continuous turning uphill and downhill, longitudinal uphill and downhill, an adaptive multi-model fusion framework is proposed. By designing the Minimum Model Error (MME) criterion based on the vehicle longitudinal dynamics model, a freight vehicle error compensation model is built to solve the problems of transverse tire force and model error under a large longitudinal slope of freight vehicles under continuous turning uphill and downhill. A robust cubature Kalman filter (RCKF) estimation method based on Chi-square distribution multi-quantile data classification detection is proposed, which can solve the problem of sensor abnormal error interference in road slope estimation and realize the robustness of the road slope estimation algorithm. The results show that the proposed method has high recognition accuracy and satisfying robustness and can achieve a robust estimation of vehicle mass and road slope under complex working conditions. © 2025 Chinese Mechanical Engineering Society. All rights reserved.