基于跳跃连接的电池的剩余使用寿命预测多尺度CNN CNN
Lin Sun1, Xiaojie Huang2, Jing Liu2
1Basic Science Department, Wenhua College, Wuhan, China. sunlin_2022_wh@163.com.
Scientific reports
|September 25, 2025
概括
这项研究引入了一种新的跳跃连接多尺度CNN模型,用于准确的离子电池剩余使用寿命 (RUL) 预测. 该模型有效地提取和融合多尺度特征,在RUL预测准确性和概括性方面表现优于现有方法.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 准确的剩余使用寿命 (RUL) 预测对于离子电池的可靠运行和维护至关重要.
- 现有的方法往往难以有效地利用深度学习模型中所有卷积层的特征信息.
- 卷积神经网络 (CNN) 是有前途的,但需要增强多层次的特征提取和融合.
研究的目的:
- 提出一种新的离子电池剩余使用寿命 (RUL) 预测模型,利用跳跃连接多尺度CNN.
- 通过从电池健康因素在不同尺度上捕获本地和全球信息来增强功能提取.
- 与现有的最先进的方法相比,提高RUL预测的准确性和概括能力.
主要方法:
- 一个跳跃连接的多尺度CNN架构被开发用于处理电池健康因素.
- 该模型采用多尺度CNN来提取各种规模的本地和全球特征.
- 一个信息融合模块整合了提取的特征,用于最终的RUL预测.
主要成果:
- 拟议的模型准确地预测了离子电池的剩余使用寿命 (RUL).
- 实验结果表明,与VMD-COA-LSTM,BiGRU-TSAM,MSTformer和MSFMTP相比,预测准确度更高.
- 该方法在RUL预测任务中展示了增强的概括能力.
结论:
- 跳跃连接多尺度CNN模型有效地利用全面的特征信息进行精确的RUL预测.
- 这种方法在离子电池健康监测和剩余寿命估计方面取得了重大进展.
- 该模型的性能验证了其在电池管理系统中的实际应用潜力.
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