Neural-Network-Based Suspension Kinematics and Compliance Characteristics and Its Implementation in Full Vehicle Dynamics Model Huazhong University of Science and Technology

Format:

Book

Conference/Event

Author/Creator:

Duan, Yupeng, author.

Contributor:

Wu, Jinglai

Zhang, Yunqing

Conference Name:

WCX SAE World Congress Experience (2022-04-05 : Detroit & Online, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

Suspension kinematics and compliance strongly influence the handling performance of the vehicle. The kinematics and compliance characteristics are determined by the suspension geometry and stiffness of suspension bodies and elastic components. However, it is usually inefficient to model all the joints, bushings, and linkage deformation in a full vehicle model. By transforming the complex modeling problem into a data-driven problem tends to be a good solution. In this research, the neural-network-based suspension kinematics and compliance model is built and implemented into a 17 DOF full vehicle model, which is a hybrid model with state variables expressed in the global coordinate system and vehicle coordinate system. The original kinematics and compliance characteristics are derived from multibody dynamics simulation of the suspension system level. The comparison of the neural network model and traditional interpolation methods is also given out to illustrate the advantages and disadvantages of these methods

Notes:

Vendor supplied data

Publisher Number:

2022-01-0287

Access Restriction:

Restricted for use by site license