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基于CEEMD-VMD和三重回声国家网络的PEMFC老化的预测

Jie Sun¹, Shiyuan Pan¹, Qi Yang¹

¹School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)

|July 12, 2025

概括

准确预测质子交换膜燃料电池 (PEMFC) 降解对于维护至关重要. 这项研究引入了与CEEMD-VMD结合的新型三重回声状态网络 (TriESN),用于准确的长期PEMFC健康预测.

科学领域:

* 能源系统工程 * 能源系统工程
* 材料科学材料科学
* 计算科学计算机科学

背景情况:

*准确预测质子交换膜燃料电池 (PEMFC) 降解对于运行效率和维护至关重要.
*动态运行条件和短期预测限制阻碍了可靠的PEMFC健康监测.
*现有的数据驱动方法在实现长期降解预测的高精度方面面临挑战.

研究的目的:

* 开发一种先进的方法,精确预测PEMFC降解轨迹.
* 提高PEMFCs剩余使用寿命预测的准确性和可靠性.
* 解决当前预测技术在动态操作环境中的局限性.

主要方法:

* 整合完整集体实证模式分解与变化模式分解 (CEEMD-VMD) 进行噪声过和特征提取.
*利用一个新的三重回声状态网络 (TriESN),增强了回声状态网络 (ESN),用于降解预测.
*TriESN架构的部分解,以减少神经元干扰,并考虑多时间尺度的衰老特征.

主要成果:

*CEEMD-VMD有效过噪音,保留关键的低频降解信息.
* 与传统的ESN相比,拟议的TriESN在长期PEMFC降解预测中显示出显著提高的准确性.
* 综合方法增强了特征表示,稀疏性和泛化能力,减少了过度拟合.

结论:

*CEEMD-VMD和TriESN的组合为准确的PEMFC降解预测提供了一个强大的解决方案.
* TriESN的部分脱结构有效地模拟了多个时间尺度的衰老,从而精确地预测了剩余的使用寿命.
*这种方法提高了PEMFC在不同操作环境中的健康监测和维护计划的可靠性.

关键词:

"回声状态网络" (Echo State Network) 是一个国家网络.预测预测预测预测燃料电池燃料电池的使用情况剩余的使用寿命.变化模式分解的变化模式分解

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