基于多模型集成的储能电池的剩余使用寿命预测方法的研究
Lei Shao1, Liangqi Zhao1, Hongli Liu1
1School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 30384, China.
准确预测离子电池 (LIB) 的剩余使用寿命 (RUL) 是至关重要的. 一种新的多模型整合方法显著提高了RUL预测的准确性和概括性,优于单个模型.
科学领域:
- 电池技术 电池技术
- 机器学习 机器学习
- 预测性维护是指预测性维护.
背景情况:
- 对离子电池 (LIB) 的准确剩余使用寿命 (RUL) 预测对于设备安全和电池管理至关重要.
- 目前的单一机器学习模型由于随机性而表现出不良的概括性,而组合方法缺乏理论依据.
研究的目的:
- 提出和验证一种新的多模型整合方法,以提高LIBs的RUL预测.
- 解决单一和现有的联合预测方法的局限性.
主要方法:
- 对长期短期记忆网络和随机森林的预测原理的分析.
- 开发用于RUL预测的多模型整合策略.
- 使用实验LIB数据进行验证.
主要成果:
- 拟议的多模型方法在最佳情况下达到0.14%的根平均平方误差 (RMSE),显示出强大的概括性.
- 与单个型号相比,平均RMSE降低了46.2%,43.7%和80.6%,表明了优越的装配性能.
结论:
- 综合多模型方法为LIBs的RUL预测准确性提供了显著的改进.
- 该方法表现出强大的概括性和适配性能,突出了其在储能电池管理方面的潜力.
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