This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Modeling and Experiment of a Heavy-Duty Truck with an Improved Maxwell-Slip Model and Iterated Improved Reduction System Method
ISSN: 2380-2162, e-ISSN: 2380-2170
Published January 27, 2020 by SAE International in United States
Citation: Tan, B., Xie, Q., Zheng, M., Zhang, B. et al., "Modeling and Experiment of a Heavy-Duty Truck with an Improved Maxwell-Slip Model and Iterated Improved Reduction System Method," SAE Int. J. Veh. Dyn., Stab., and NVH 4(1):19-36, 2020, https://doi.org/10.4271/10-04-01-0002.
Since vehicle structural flexibility and suspension nonlinearity are usually not considered, many existing vehicle models have difficulty in accurately describing the dynamic characteristics of the actual vehicle, which limits their practical applications. This article presents a rigid-flexible coupled system to investigate the dynamic behavior of a heavy-duty truck. An improved Maxwell-slip (IMS) model is proposed to describe the hysteresis nonlinearity of a leaf spring. In the coupled system, the axles and powertrain are simplified to be rigid, and the cab and frame are modeled using finite element method (FEM) considering their flexibility. During the solution process, the application of the FEM leads to a significant increase in the computer burden. Therefore, the iterated improved reduction system (IIRS) method is adopted to reduce the size of the large-size finite-element (FE) models to achieve the purpose of improving the calculation efficiency. Furthermore, the actual leaf spring and vehicle experiments are conducted to verify the accuracy of the proposed IMS model and full-vehicle model. Numerical simulations and experimental results show that the proposed IMS model can accurately describe the hysteresis nonlinearity of leaf springs and the full truck model can well predict the vibration behavior of the actual vehicle. In addition, the IIRS method can truncate a large number of DOFs without compromising the accuracy of the model, so that the computing efficiency is improved significantly.