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离子电池的RUL预测基于优化的VMD-SSA-PatchTST算法

Pei Tang¹, Zetao Qiu², Zhongran Yao³

¹School of Automotive Engineering, Yancheng Institute of Technology, Yanchen, 224051, China.

Scientific reports

|July 23, 2025

概括

这项研究引入了一种新的方法,通过将信号分解与PatchTST模型相结合来预测离子电池剩余使用寿命 (RUL). 先进的WOA-VMD-SSA-PatchTST方法显著提高了RUL预测的准确性和可靠性.

科学领域:

电池工程电池工程
人工智能的人工智能
信号处理信号处理

背景情况:

准确的剩余使用寿命 (RUL) 预测对于离子电池的安全性和系统可靠性至关重要.
现有的方法经常与电池复杂的降解模式作斗争.

研究的目的:

为增强离子电池RUL预测开发一种新的预测框架.
将模态分解技术与PatchTST深度学习模型集成,以提高准确性.

主要方法:

斯皮尔曼相关性确定了与电池容量相关的关键特征.
变化模式分解 (VMD),通过鱼优化算法 (WOA) 进行优化,分解了容量序列.
通过Sparrow搜索算法 (SSA) 调整的超参数的PatchTST网络预测了RUL.

主要成果:

拟议的WOA-VMD-SSA-PatchTST模型与基线模型 (CNN,GRU,PatchTST) 相比显示出更高的性能.
在NASA数据集上的实验验证证证了该模型增强的预测准确性和稳定性.

结论:

模式分解和PatchTST的融合为电池RUL预测提供了一个强大的方法.
这一框架显著提高了离子电池系统的可靠性和安全性.

关键词:

离子电池是一种离子电池.补丁时间序列变压器.剩余的使用寿命.变量模式分解变量模式分解

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