基于智能电动汽车驾驶风格识别的适应天气的再生制动策略
Marwa Ziadia1, Sousso Kelouwani1, Ali Amamou1
1Department of Mechanical Engineering, Hydrogen Research Institute, University of Québec at Trois-Rivières, Trois-Rivieres, QC G8Z 4M3, Canada.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了电动汽车的天气适应性再生制动策略 (WARBS),以改善各种天气条件下的能源效率. 该系统优化再生制动,提高能量回收,即使在滑动的道路上.
科学领域:
- 汽车工程 汽车工程
- 能源系统 能源系统
- 人工智能的人工智能
背景情况:
- 先进的驾驶辅助系统 (ADAS) 往往优先考虑安全,而不是能源效率.
- 电动汽车的再生制动在恶劣的天气条件下,如雪和冰,效果较差.
- 在制动过程中优化能量回收对于电动汽车的续航里程和性能至关重要.
研究的目的:
- 开发和评估适应天气的再生制动策略 (WARBS),以提高电动汽车的能源效率.
- 整合驾驶风格和实时天气/道路条件,以实现自适应式制动控制.
- 为了最大限度地减少能源损失,并在各种环境场景中最大限度地提高再生制动的有效性.
主要方法:
- 开发包含天气和道路摩擦数据的驾驶风格识别模型.
- 关于适应减速计划的建议,以最大限度地将动能转化为电能.
- 实施驾驶上下文识别系统,以在不同天气条件下优化速度规划.
主要成果:
- 通过模拟和实验,WARBS系统证明了整体能源效率的显著提高.
- 适应性策略有效地改善了低摩擦表面的再生制动性能.
- 整合驾驶背景识别有助于更好的速度规划和能量回收.
结论:
- 拟议的天气适应性再生制动策略 (WARBS) 有效地提高了电动汽车在恶劣天气中的能效.
- 基于实时条件和驾驶风格的自适应控制是最大化再生制动性能的关键.
- 这种方法提供了一个可行的解决方案,以提高电动汽车的实用性和范围.
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