Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses
Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/22/10310 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846154519151378432 |
|---|---|
| author | Lexuan Liu Xiurui Guo Xinyu Yang Lijun Liu |
| author_facet | Lexuan Liu Xiurui Guo Xinyu Yang Lijun Liu |
| author_sort | Lexuan Liu |
| collection | DOAJ |
| description | Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness. |
| format | Article |
| id | doaj-art-1781f79d68334b22a3b5a1ee5f84e71d |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1781f79d68334b22a3b5a1ee5f84e71d2024-11-26T17:48:15ZengMDPI AGApplied Sciences2076-34172024-11-0114221031010.3390/app142210310Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle ResponsesLexuan Liu0Xiurui Guo1Xinyu Yang2Lijun Liu3The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaDepartment of Civil Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Civil Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Civil Engineering, Xiamen University, Xiamen 361005, ChinaHighways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness.https://www.mdpi.com/2076-3417/14/22/10310vehicle parameter identificationroad roughness identificationextended Kalman filter with unknown inputsvehicle responses |
| spellingShingle | Lexuan Liu Xiurui Guo Xinyu Yang Lijun Liu Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses Applied Sciences vehicle parameter identification road roughness identification extended Kalman filter with unknown inputs vehicle responses |
| title | Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses |
| title_full | Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses |
| title_fullStr | Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses |
| title_full_unstemmed | Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses |
| title_short | Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses |
| title_sort | combined identification of vehicle parameters and road surface roughness using vehicle responses |
| topic | vehicle parameter identification road roughness identification extended Kalman filter with unknown inputs vehicle responses |
| url | https://www.mdpi.com/2076-3417/14/22/10310 |
| work_keys_str_mv | AT lexuanliu combinedidentificationofvehicleparametersandroadsurfaceroughnessusingvehicleresponses AT xiuruiguo combinedidentificationofvehicleparametersandroadsurfaceroughnessusingvehicleresponses AT xinyuyang combinedidentificationofvehicleparametersandroadsurfaceroughnessusingvehicleresponses AT lijunliu combinedidentificationofvehicleparametersandroadsurfaceroughnessusingvehicleresponses |