Online Identification of Hybrid Tractor Tire-soil Interaction Model Based on Multiple Filtering Joint Observation
Abstract
Accurate tire-soil models are fundamental to optimizing the traction force control of hybrid tractors, thereby improving overall energy efficiency. Among these, precise estimation of the longitudinal and vertical forces is crucial for ensuring model identification accuracy. However, during operation, terrain variations and the unknown load changes caused by the interaction between the plow body and the soil lead to significant fluctuations in the longitudinal and vertical forces on each tire, which increases the difficulty of accurate estimation. To address this, a tire-soil interaction model identification method based on the fusion of multiple filters and joint observation was proposed, taking the distributed hybrid electric tractor (DHET) as research object. Firstly, the longitudinal force of the tire was estimated by using the Kalman filter (KF), and the vertical force was estimated by using the cubature Kalman filter (CKF) algorithm combined with the dynamic model. Then, based on the Brixius model and the vehicle’s longitudinal dynamics, an online identification device for the tire-soil model was constructed by using the unscented particle filter (UPF). The unscented transformation (UT) was used to design the particle proposal distribution, preserving higher-order information in the nonlinear tire-soil model, and the particle filter (PF) algorithm was applied to update the estimated parameters, completing the model’s online identification. Hardware-in-the-loop (HIL) test results showed that the method can quickly and accurately achieve identification under varying terrain conditions, with a root mean square error not exceeding 1.2. Experimental results indicated that, under two types of soil conditions, the identification model’s boundary traction coefficient error was within ±0015, with the front wheel test data distribution accounting for 84.45% and 88.16%, respectively, and the rear wheel data for 86.72% and 85.38%, verifying that this method maintained high accuracy and robustness under different soil conditions. The research findings can provide theoretical support for the optimal drive force distribution strategy of tractors and contribute to improving traction efficiency and fuel economy.
Keywords: hybrid electric tractor;unscented particle filtering;cubature Kalman filtering;wheel force estimation;tire-soil interaction model
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