Proton exchange membrane fuel cell (PEMFC) stacks are widely used in mobile and portable applications due to their clean and efficient model of operation. We propose an ensemble model based on a stacked long short‐term memory model that combines three machine‐learning models, including long short‐term memory with attention mechanism, support vector regression, and random forest regression, to improve the degradation prediction of a PEMFC stack. The prediction intervals can be derived using the dropout technique. The proposed model is compared with some existing models using two PEMFC stacks. The results show that the proposed model outperforms the other models in terms of mean absolute percentage error and root mean square error. Regarding the remaining useful life prediction, the proposed model with the sliding window approach can provide better results.

中文翻译：

---

质子交换膜燃料电池堆退化预测的集成模型

质子交换膜燃料电池（PEMFC）堆由于其清洁高效的运行模型而广泛用于移动和便携式应用中。我们提出了一个基于堆叠的长期短期记忆模型的集成模型，该模型结合了三种机器学习模型，包括具有注意机制的长期短期记忆，支持向量回归和随机森林回归，以改善PEMFC的退化预测堆栈。可以使用辍学技术来得出预测间隔。将所提出的模型与使用两个PEMFC堆栈的一些现有模型进行比较。结果表明，该模型在平均绝对百分比误差和均方根误差方面均优于其他模型。关于剩余使用寿命预测，