使用机器学习方法的离子电池智能故障检测 (IFD) 系统
Rahul Kumar Kamboj1, Mukesh Singh2, Ashima Singh3
1Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Street, Patiala, Punjab, 147004, India. rkamboj_phd22@thapar.edu.
Scientific reports
|September 1, 2025
概括
智能故障检测系统通过识别热保护,低压和过压等问题来提高电动汽车 (EV) 电池的安全性. 这种机器学习方法提高了电动汽车的可靠性和可持续性.
科学领域:
- 电气工程
- 材料科学
- 计算机科学
背景情况:
- 由于环境问题和效率问题,电动汽车越来越受欢迎.
- 电动汽车的电池故障会带来严重的安全风险.
- 现有的故障检测方法需要加强主动安全.
研究的目的:
- 为电动汽车电池引入智能故障检测系统.
- 开发和实施基于机器学习 (ML) 的机制来监控和保护电动汽车电池.
- 解决关键故障,包括热保护,低压和过压.
主要方法:
- 在车辆运行期间从嵌入式电池传感器收集实时数据.
- 应用K-means集群算法进行数据分析,并定义温度和电压的有效操作范围.
- 使用组合方法来分类安全和不安全的电池运行条件.
主要成果:
- 拟议的基于ML的IFD系统在识别电池故障时达到0.94的准确性.
- 该系统有效地监控和保护电池免受关键故障.
- 通过数据集群定义了温度和电压的有效操作范围.
结论:
- 开发的IFD系统显著提高了电动汽车电池的安全性和可靠性.
- 这种主动的故障检测有助于电动汽车的长期可持续性和经济可行性.
- 智能技术为管理电动汽车电池健康和性能提供了强大的解决方案.
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