混合机器学习框架用于预测性维护和在离子电池中检测异常,使用增强的随机森林
R Seshu Kumar1, Arvind R Singh2, P Lakshmi Narayana1
1Department of Electrical and Electronics Engineering, Vignan's Foundation for Science Technology and Research (VFSTR Deemed to Be University), Vadlamudi, 522213, India.
Scientific reports
|February 20, 2025
概括
本研究介绍了一种改进的随机森林算法,用于离子电池的预测性维护,增强电池管理系统. 该框架确保实时健康诊断和准确的状态估计,以提高安全性和寿命.
科学领域:
- 材料科学与工程 材料科学与工程
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 离子电池对于电动汽车和储能至关重要,需要先进的预测性维护.
- 目前的电池管理系统在实时健康诊断和充电状态估计方面存在局限性.
研究的目的:
- 为离子电池开发一个全面的预测性维护框架.
- 通过使用改进的随机森林算法来改进电池健康评估和充电状态估计.
主要方法:
- 整合基于物理的方法与数据驱动的机器学习模型.
- 使用改进的随机森林 (IRF) 算法进行动态电池健康评估.
- 分析包括充电状态,能源效率和产能下降等特征.
主要成果:
- 该IRF算法比梯度提升和标准随机森林实现了更高的性能,根平均平方误差为1.575和R2为0.9995.5.
- 实现了99.99%的分类准确度,用于检测异常,没有虚假阴性.
- 证明了实时适应性和强大的异常检测能力.
结论:
- 开发的框架通过提高离子电池的可靠性,安全性和寿命,为可持续能源系统提供了变革性的解决方案.
- 该IRF模型提供了准确的预测,并促进了主动干预,降低了运营风险.
- 该框架解决了可扩展性和计算效率的挑战,将其定位为现代电池应用的关键工具.
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