基于联合模拟和机器学习的发动机冷却系统的电子风扇和水的性能预测
Yujin Zou1,2, Renwang Li1, Honghua Pan3
1School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China.
Scientific reports
|May 5, 2025
概括
这项研究开发了一种校准的发动机冷却系统模型. 支持向量回归 (SVR) 准确地预测了水速度,而随机森林 (RF) 在预测智能冷却管理的风扇速度方面表现出色.
科学领域:
- 汽车工程 汽车工程
- 热力学是一种热力学.
- 机器学习应用 机器学习应用
背景情况:
- 发动机冷却系统的性能对于车辆的效率和响应性至关重要.
- 精确预测风扇和速度对于优化冷却系统运行至关重要.
- 现有的模型可能缺乏实时智能管理所需的精度.
研究的目的:
- 为详细的发动机冷却系统开发和校准一个联合模拟模型.
- 评估四个机器学习模型对水和风扇速度的预测性能.
- 为智能发动机冷却系统管理建立一个强大的建模基础.
主要方法:
- 开发了发动机冷却系统的联合模拟模型.
- 使用车辆道路循环测试对模型进行校准.
- 系统评估支持向量回归 (SVR) 和随机森林 (RF) 模型用于速度预测.
主要成果:
- 校准的模拟模型在转速方面实现了不到2%的偏差,在冷却系统结果方面达到5%的偏差.
- SVR模型显示了水速度预测的高精度 (MSE=0.03986).
- 射频模型在没有过度装配的情况下展示了风扇速度预测 (MSE=0.00052) 的卓越性能.
结论:
- 校准的模拟模型准确地反映了真实世界的驾驶条件和燃料消耗.
- 这些SVR和RF模型为水和风扇的速度分别提供了准确和可靠的预测.
- 这些发现支持开发发动机冷却系统的智能管理策略.
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