提出一种基于PatchTST模型的燃料电池老化趋势预测方法,该方法通过将时间序列数据划分为多个局部时间窗口,并结合Transformer结构捕捉长短期依赖关系,可实现对燃料电池老化趋势的精确预测。在实验中,选取稳态和准动态工况下,分别采用训练集占比为50%、60%和70%的数据进行模型训练,并预测未来50 h、100 h和150 h的老化趋势。通过URMSE和UMAE等误差评估指标分析结果表明,当FC1数据集的训练集占比为60%、FC2数据集的训练集占比为50%时,模型的预测误差最小。尽管随着预测时长的增加误差有所增大,但整体表现仍较为稳定。在FC1数据集分别按50%和60%比例划分的预测条件下,PatchTST模型的预测误差小于Informer、Trasnformer、GRU和LSTM模型。
Abstract
This paper proposes a fuel cell aging trend prediction method based on the PatchTST model. By segmenting time series data into multiple local time windows and combining the Transformer architecture to capture long-and short-term dependencies, the method achieves precise prediction of fuel cell aging trends. In the experiments, the model was trained under steady-state and quasi-dynamic operating conditions using data with training set proportions of 50%, 60%, and 70%, respectively, to predict aging trends for future horizons of 50 h, 100 h, and 150 h. Analysis based on error evaluation metrics, such as URMSE and UMAE, indicates that the model achieves the lowest prediction error when the training set proportion is 60% for the FC1 dataset and 50% for the FC2 dataset. Although the error increases slightly as the prediction horizon extends, the overall performance remains relatively stable. Under the conditions where the FC1 dataset is divided by 50% and 60% ratios, the prediction errors of the PatchTST model are lower than those of the Informer, Transformer, GRU, and LSTM models.
关键词
燃料电池 /
时序分析 /
老化 /
预测 /
自注意力机制 /
PatchTST
Key words
fuel cell /
time series analysis /
degradation /
prediction /
self-attention mechanism /
PatchTST
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基金
安徽省自然科学基金（2308085ME180）; 广东恒翼能科技股份有限公司合作项目（W2023JSFW0479）; 高等学校学科创新引智计划 （“111”计划）资助项目（BP0719039）
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