动态太阳辐射估计用于车辆热管理,使用多模式机器学习框架
Rial A Rajagukguk1, Hoseong Lee2, Hyunjin Lee3
1Department of Mechanical Engineering, Kookmin University, Seoul, 02707, Republic of Korea.
Scientific reports
|December 8, 2025
概括
通过新的机器学习框架,现在可以对移动车辆进行精确的太阳辐射估计. 这项技术提高了电动汽车 (EV) 的热管理和能源效率.
科学领域:
- 环境科学 环境科学
- 汽车工程 汽车工程
- 数据科学数据科学数据科学
背景情况:
- 准确的太阳辐射估计对于车辆的热管理至关重要,特别是对于电动汽车 (EV),因为它会影响电池寿命和续航里程.
- 现有的方法在车辆移动期间与动态条件作斗争,限制了现实世界的适用性.
研究的目的:
- 开发一种新的机器学习框架,用于在移动车辆周围实时估计太阳辐射.
- 改进车辆的热管理和能源优化策略.
主要方法:
- 整合卫星遥感和天空摄像机图像.
- 使用来自带有传感器的仪器车辆的现场数据进行验证 (太阳辐射,GPS,环境).
- 融合多源环境数据,实现高时空分辨率的预测.
主要成果:
- 该框架在夏季达到14.61%,冬季达到17.10%的正常化平方根平均误差,实现了强的表现.
- 在各种条件下提供了太阳辐射的高时空分辨率预测.
- 该模型在各种路线和季节条件下证明了有效性.
结论:
- 拟议的框架为移动车辆提供准确的实时太阳辐射估计.
- 能够实现适应性,位置意识的热管理策略,以提高能源效率和乘客舒适度.
- 卫星和地面数据的整合确保了各种地区和车辆类型的可扩展性.
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