Research on on-board dynamic weighing algorithm based on two-degree-of-freedom 1/4 vehicle model Online publication date: Thu, 07-Apr-2022
by Hongxun Fu; Yu Zhang; Xianyue Gang; Huanbo Qiao; Yan Wang; Laiyun Ku
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 28, No. 6, 2021
Abstract: The moving vehicle will be disturbed in many aspects, resulting in the dynamic weighing accuracy of the airborne weighing system being significantly lower than the static accuracy. In order to improve the dynamic weighing accuracy of the system, this paper designs a dynamic weighing algorithm based on wavelet threshold denoising and BP neural network. Firstly, a two-degree-of-freedom 1/4 vehicle model was built to obtain the vehicle dynamic distance data. Then, the wavelet threshold denoising algorithm was used to denoise the dynamic distance data. Finally, the BP neural network was constructed with the signal of vehicle speed, acceleration signal and denoised weight signal as the input layer to reduce the impact of the speed and acceleration on the weight signal. The results show that after the processing of dynamic weighing algorithm, the dynamic weighing error of vehicle is less than 2%, and the algorithm meets the accuracy requirements, and has high universality.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Heavy Vehicle Systems (IJHVS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook