Overload Identification System Based on Vibration State of Two-Axle Vehicle
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- Provide download link
- Format
- Price
- Non-members (tax incl.)：￥6,600 Members (tax incl.)：￥5,280
- Paper/Info type
- SAE Paper
No.2021-01-0172
- Pages
- 1-11(Total 11 p)
- Date of publication
- Apr 2021
- Publisher
- SAE International
- Language
- English
- Event
- SAE WCX Digital Summit 2021
Detailed Information
| Author(E) | 1) Wenchao Sun, 2) Gangfeng Tan, 3) Xingguo Han, 4) Feng‘an Zhao, 5) Meng Ye, 6) Jiaming Feng |
|---|---|
| Affiliation(E) | 1) Wuhan University of Technology, 2) Wuhan University of Technology, 3) Hubei Public Security Department, 4) Wuhan University of Technology, 5) Wuhan University of Technology, 6) Wuhan University of Technology |
| Abstract(E) | The non-contact overload recognition method refers to the method of detecting the vibration state of the vehicle through visual recognition without touching the vehicle, and then calculating the vehicle load in combination with the vehicle dynamics model to determine whether the passing vehicle is overloaded. Due to the convenience of detection, low cost of infrastructure and informatization, this method has great advantages in the field of overload identification. However, the model used in this recognition method is the single mass vibration model at present, which will have a large error due to the interaction between the front and rear suspension, and the position of the center of mass needs to be acquired in the recognition process, which is difficult in the actual identification process. In this paper, a vehicle vibration model containing two modes of vibration is proposed, and uses Sobol algorithm to analyze the parameter sensitivity of the model. According to the analysis results, the model is optimized to reduce the influence of vehicle centroid position on the model. After Carsim simulation, the HQG1043EV5 pure electric vehicle was taken as an experimental vehicle for experiment. In the experiment, the parameters of the vehicle vibration were captured by the side camera. After judging the vibration of the driving vehicle, the obtained parameters were put into the corresponding mathematical model to calculate the vehicle load and further judge whether the vehicle was overloaded. The results of simulation and experiment show that the maximum error range of this method is 9.63%-19.76%, and the average value of the maximum error is 15.98%, which is a great improvement compared with the single-mass vibration model. After optimizing the vehicle model, the recognition error is reduced, and it is hoped that the method can replace the traditional weighing methods in a certain research stage, or provide redundant backup for other new identification methods. This method has positive significance for the development of non-contact overload identification technology. |
