一个增强的神经网络模型用于预测质子交换膜燃料电池的剩余使用寿命
Honghua Pan1, Yujin Zou2,3, Xiao Sun1
1Department of Information Technology, Zhejiang Institute of Economics and Trade, Hangzhou, 310018, Zhejiang, China.
Scientific reports
|October 24, 2025
概括
一种新的GMA方法准确地预测了质子交换膜燃料电池 (PEMFC) 的电压和剩余使用寿命 (RUL). 最佳的输入/输出大小可以提高燃料电池维护的预测准确性和稳定性.
科学领域:
- 能源系统工程 能源系统工程
- 电化学工程 电化学工程
- 材料科学 材料科学 材料科学
背景情况:
- 质子交换膜燃料电池 (PEMFCs) 对清洁能源至关重要,但预测它们的性能退化和剩余使用寿命 (RUL) 是具有挑战性的.
- 准确的预测对于有效的维护计划,性能评估和PEMFC系统的生命周期管理至关重要.
研究的目的:
- 提出和评估基于高斯混合自编码器 (GMA) 的方法,用于预测PEMFC堆电压和RUL.
- 分析不同输入和输出大小对GMA模型预测性能的影响.
主要方法:
- 为PEMFC电压和RUL预测量身定制的GMA模型的开发.
- 对不同的输入和输出数据配置进行系统分析,以确定它们对模型准确性的影响.
- 使用诸如根平均平方误差 (RMSE) 和性能得分等指标评估模型性能.
主要成果:
- 该GMA模型有效地捕捉了PEMFC电压降解趋势,包括早期的快速下降和以后的平稳降解.
- 模型性能对输入和输出大小选择非常敏感.
- 最佳性能,以最低的RMSE和近乎最佳的得分为特征,在输入大小为300和输出大小为40时实现.
- 较大的输入大小通常通过提供更多信息功能来提高预测准确性.
结论:
- 在提高PEMFC预测的准确性和可靠性方面,GMA方法显示了显著的优势.
- 仔细选择输入和输出大小对于提高GMA模型的预测效率和稳定性至关重要.
- 这项研究为PEMFC RUL预测提供了实际框架,有助于维护,评估和管理.
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