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Updated: May 14, 2025

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基于离散波段变换和脱的回声状态网络的PEMFCs降解预测

Jie Sun¹, Wenshuo Li², Mengying He³

¹School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

这项研究引入了一种新的方法,用于预测使用离散波量变换和脱回声状态网络 (DESN-Z) 的质子交换膜燃料电池 (PEMFC) 降解. 该DDESN-Z方法显著改善了长期PEMFC健康监测和维护计划的准确性.

科学领域:

能源系统工程能源系统工程
计算科学计算科学
材料科学材料科学材料科学

背景情况:

精确预测质子交换膜燃料电池 (PEMFC) 降解对于有效的维护和健康监测至关重要.
动态的运行条件和当前预测方法的局限性阻碍了精确的PEMFC降解预测.

研究的目的:

开发一个先进的PEMFC降解预测模型,提高准确性,特别是长期预测.
将离散波量变换 (DWT) 与新型脱回声状态网络 (DESN-Z) 集成,以改进PEMFC性能预测.

主要方法:

利用离散波波变换 (DWT) 来减少噪声,并从PEMFC操作数据中提取特征.
引入了一种新的脱回声状态网络,具有递减抑制机制 (DESN-Z),以改进预测准确度.
结合DWT和DESN-Z (DDESN-Z) 以利用增强的特征表示和网络泛化.

主要成果:

DDESN-Z模型有效地减轻了噪音和过度装配,从而改善了特征表示和稀疏性.
在静态,准动态和完全动态的操作场景中,在长期PEMFC退化预测中显著提高了准确性.
通过管理神经元相互连接,DESN-Z中的侧向抑制机制加速了信息获取,并通过管理神经元相互连接来完善预测.

结论:

DDESN-Z方法为准确的长期PEMFC降解预测提供了强大的解决方案.
这种方法提高了PEMFC健康监测和维护计划的可靠性.
该研究强调了将信号处理技术与先进的神经网络相结合的潜力,以实现复杂系统预测.

关键词:

降解降解预测的预测.离散的波形变换."回声状态网络" (Echo State Network) 是一个国家网络.燃料电池燃料电池的使用情况剩余的使用寿命.

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