1 option
Data-Driven Study on Chassis Suspension Performance Degradation Tsinghua University and BYD Auto Company, Limited
- Format:
- Book
- Conference/Event
- Author/Creator:
- Liao, Yinsheng, author.
- Lei, Yisong, author.
- Su, Ailin, author.
- Wang, Zhenfeng, author.
- Shi, Shuai, author.
- Zhang, Lei, author.
- Zhang, Junzhi, author.
- Ma, Changye, author.
- Conference Name:
- WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)
- Language:
- English
- Subjects (All):
- Chassis stiffness.
- Dampers and shock absorbers.
- Neural networks.
- Suspension systems.
- Vehicle handling.
- Vehicle ride.
- Vehicle performance.
- Comfort.
- Roads and highways.
- Springs.
- Local Subjects:
- Chassis stiffness.
- Dampers and shock absorbers.
- Neural networks.
- Suspension systems.
- Vehicle handling.
- Vehicle ride.
- Vehicle performance.
- Comfort.
- Roads and highways.
- Springs.
- Physical Description:
- 1 online resource
- Place of Publication:
- Warrendale, PA SAE International 2026
- Summary:
- The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their degradation characteristics. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental results show that the maximum absolute percentage errors of the DNN model in predicting suspension stiffness and damping coefficients are less than 0.13% and 0.17%, respectively, with average absolute percentage errors below 0.046% and 0.06%. The coefficients of determination (R2) exceed 0.999. The proposed method accurately predicts the trend of key suspension parameters, providing robust data support for health management and maintenance decision-making. This is expected to reduce safety risks and maintenance costs while enhancing overall vehicle performance and reliability
- Notes:
- Vendor supplied data
- Access Restriction:
- Restricted for use by site license
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.