As a promising energy conversion device, the proton exchange membrane fuel cell (PEMFC) has been widely used in many fields. However, its commercialization is limited by its useful lifetime, so it's very important to predict the remaining useful lifetime (RUL). In this paper, an RUL prediction method of PEMFC based on convolutional neural network (CNN) and long short-term memory (LSTM) is proposed. First, for data processing, we use Savitzky-Golay to smooth the datasets, a box plot to remove the outliers, and Z-score to normalize the datasets. Then, we perform experiments on different lengths of time series data to find the best parameters and test the generalization ability of the model to long-term and short-term forecasts. Eventually, the results indicated that CNN-LSTM and CNN-bidirectional LSTM (CNN-BiLSTM) can get very accurate predictions with the relative error values of CNN-LSTM being 0.07% and CNN-BiLSTM only 0.03%. Furthermore, we discover that the training and prediction speed of the models are improved due to the addition of CNN. Therefore, we can quickly and accurately predict the RUL of PEMFC in the long term and short term.

中文翻译：

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基于卷积神经网络-长短期记忆和卷积神经网络-双向长短期记忆的质子交换膜燃料电池剩余寿命预测方法

质子交换膜燃料电池（PEMFC）作为一种很有前途的能量转换装置，在许多领域得到了广泛的应用。然而，其商业化受到其使用寿命的限制，因此预测剩余使用寿命（RUL）非常重要。本文提出了一种基于卷积神经网络（CNN）和长短期记忆（LSTM）的PEMFC RUL预测方法。首先，对于数据处理，我们使用 Savitzky-Golay 平滑数据集，使用箱线图去除异常值，使用 Z-score 对数据集进行归一化。然后，我们对不同长度的时间序列数据进行实验，以找到最佳参数并测试模型对长期和短期预测的泛化能力。最终，结果表明，CNN-LSTM 和 CNN-双向 LSTM (CNN-BiLSTM) 可以得到非常准确的预测，CNN-LSTM 的相对误差值为 0.07%，而 CNN-BiLSTM 仅为 0.03%。此外，我们发现由于添加了 CNN，模型的训练和预测速度得到了提高。因此，我们可以快速准确地预测PEMFC的长期和短期RUL。