Traction Resistance Estimation Based on Multi-Method Fusion for Distributed Drive Agricultural Vehicles - Sun, Chenyang; Zhou, Jun; Zhao, Jianlei (IEEE sensors journal, 2022)
This study proposes a multi-method fusion algorithm to solve the problem of coupled traction resistance with the vehicle mass for distributed drive agricultural vehicles (DDAVs), because this coupling makes estimation measurements by agricultural vehicles difficult. Indeed, the proposed method decouples the vehicle mass and traction resistance. The vehicle mass was obtained using the recursive least square method and filtering the low-frequency parts of signals of driving force and longitudinal acceleration. After obtaining estimated vehicle mass, the dynamics method was coordinated and complemented with the kinematics method to observe the traction resistance. In the low-frequency load test, statistical performance criteria (SPCs) of the mass estimation were R = 0.9985 , root mean squared error ( RMSE) = 0.0551 kg, and average of prediction accuracy ( PA) = 98.02%. In addition, SPCs of the traction resistance estimation were R=0.9655 , RMSE = 23.0472 N, and average of PA = 99.28%. In the high-frequency load test, the maximum PA of the mass estimation reached 98.78%, and SPCs of the traction resistance estimation were R = 0.9371 , RMSE = 1266.3933 N, and average of PA = 85.62%. Experimental tests proved that the proposed method is robust and can accurately estimate the vehicle mass and traction resistance in real time.