对离子电池的降解模型和剩余使用寿命预测进行审查,以测试离子电池的设计和预测维护
Gabriele Patrizi1, Luca Martiri2, Antonio Pievatolo3
1Department of Information Engineering, University of Florence, 50139 Florence, Italy.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究引入了加速降解测试和预测性维护的新框架,使用先前的知识和数据. 它提高了系统可靠性和寿命预测,特别是在离子电池.
科学领域:
- 可靠性工程可靠性工程
- 数据科学数据科学数据科学
- 电池技术 电池技术
背景情况:
- 加速降解测试和预测性维护对于系统可靠性至关重要.
- 现有的方法可能无法充分利用先前的知识或有效地整合各种数据源.
- 离子电池需要先进的方法来预测性能和计划维护.
研究的目的:
- 为加速降解测试和预测性维护开发一种新的决策框架.
- 整合先前的知识和实验数据,以进行增强的系统状态评估.
- 为优化生命测试实验和维护策略提供强大的工具.
主要方法:
- 利用动态编程和强化学习来进行顺序决策.
- 集成的数据驱动的降解学习.
- 集成的随机和机器学习降解模型,使用数据驱动方法.
主要成果:
- 展示了加速降解测试和预测性维护的新框架.
- 成功地整合了先前的知识和实验数据,用于系统状态分析.
- 展示了该框架在设计寿命测试实验和维护离子电池方面的实用性.
结论:
- 拟议的框架为优化可靠性和维护策略提供了一个强有力的方法.
- 数据驱动的退化模型和顺序决策增强了预测能力.
- 这种方法对于延长离子电池的使用寿命尤其有价值.
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