期刊
IEEE ACCESS
卷 9, 期 -, 页码 24872-24883出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3057494
关键词
Hydraulic systems; Hydrodynamic retarder; back-propagation neural network; constant-torque braking; temperature performance
资金
- National Natural Science Foundation of China [51475041, 51805027]
- Beijing Institute of Technology
- Student Training Scholarship
Excessively high brake temperatures of hydrodynamic retarders can result in brake fading and failure, reducing brake effectiveness. Constructing temperature models for hydrodynamic retarders is challenging due to the system's non-linear characteristics. A model based on an artificial neural network has been shown to accurately predict temperature performance in constant-torque braking processes.
Excessively high brake temperature of hydrodynamic retarders may lead to brake fading and failure, resulting in a decrease in brake effectiveness. However, the temperature performance modeling of hydrodynamic retarders is a challenge because of the non-linear characteristics of the system. In this study, a temperature model based on an artificial neural network is constructed to predict the temperature performance of a hydrodynamic retarder in constant-torque braking process. The model is developed from a back-propagation neural network trained with the Levenberg-Marquardt algorithm. Before the application of the neural network, computational fluid dynamics is used to obtain the controllable region where experimental tests were performed to collect data for neural network training and validation. The linear regression method is adopted to check the quality of the training. It is shown that the constructed back-propagation neural network model is within 98% accuracy. Furthermore, the temperature model of the hydrodynamic retarder, which consists of the back-propagation neural network and thermal balance models, is simulated for 1500N.m, 2000N.m, and 2500N.m constant-torque braking processes. The simulation results are in agreement with experimental data within 2.87% error. The proposed temperature model can predict the temperature of the hydrodynamic retarder accurately and provide theoretical guidance for brake control strategy and thermal management.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据