
质子交换膜燃料电池(proton exchange membrane fuel cell
PEMFC)作为一种高效清洁的氢能发电装置,其运行过程中不可避免地存在性能退化与突变现象,亟需开展高精度退化预测以支撑故障预测与健康管理。针对传统数据驱动方法在退化突变阶段预测滞后、误差较大的问题,提出了一种ASW(adaptive sliding window)增强型MLP(multilayer perceptron)退化预测方法。该方法在多层感知器模型的基础上引入自适应滑动窗口机制,通过动态引入突变前的关键信息,有效抑制由工况变化或人为因素引起的剧烈数据波动,从而降低退化突变阶段的预测延迟与不确定性。实验结果表明,在保证数据完整性且避免信息泄露的前提下,引入ASW机制后,模型在静态与动态数据集上的均方根误差分别降低14.3%和10.8%,显著优于传统MLP模型。研究结果表明,所提出的ASW增强型MLP模型能够有效提升PEMFC退化预测的精度与鲁棒性,为燃料电池系统的健康管理提供了一种具有工程应用价值的数据驱动方法。
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