新兴的传感器技术和以物理为导向的方法用于监测汽车电池
1Electromobility Research Centre (MOBI), Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, Brussels, Belgium.
Communications engineering
|March 12, 2025
概括
先进的传感器和物理引导的机器学习是评估电动汽车电池健康和安全的关键. 本综述探讨了传感器技术及其用于改进电池管理系统的整合.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 汽车行业向电动推进的过渡需要强大的方法来评估离子电池的健康和安全.
- 当前的电池管理系统需要增强监控能力,以解决复杂的内部动态和潜在的故障.
研究的目的:
- 为电动汽车电池管理提供传感器技术和物理引导机器学习的全面概述.
- 探索多传感器系统与先进的计算方法的集成,以改善电池监控和建模.
- 确定这些综合系统的开发和可扩展性的挑战和机会.
主要方法:
- 审查传感器技术的进步,包括机械,气体和超声波成像.
- 探索整合物理引导机器学习与多传感器数据的探索.
- 分析系统原型和扩展的挑战和机遇.
主要成果:
- 传感器技术为离子电池结构和动态提供了关键的见解.
- 物理引导的机器学习在与多传感器数据相结合时,可以提高电池建模和监控的准确性.
- 原型和扩展带来了重大挑战,但也为先进的电池管理带来了未来的潜力.
结论:
- 多种传感器和物理引导机器学习的集成对于下一代电动汽车电池管理系统至关重要.
- 解决目前系统集成和可扩展性的局限性对于实现这些技术的全部潜力至关重要.
- 未来的研究应该集中在强大的原型和扩展战略上,以确保可靠和安全的电动汽车运行.
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