通过机器学习优化 PEM 燃料电池催化剂层的组成:内部实验数据的启示

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-11-01 DOI:10.1016/j.egyai.2024.100439
Yuze Hou, Patrick Schneider, Linda Ney, Nada Zamel
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引用次数: 0

摘要

催化剂层(CL)是质子交换膜(PEM)燃料电池的重要组成部分,对性能和耐用性都有重大影响。其油墨成分可通过铂(Pt)含量、铂/碳比率和离子聚合物/碳比率来简明描述。CL 中每种物质的含量都必须精确平衡,以达到最佳运行状态。在这项工作中,我们应用人工神经网络(ANN)模型,根据阴极 CL 的成分预测 PEM 燃料电池的性能和耐用性。该模型是根据我们实验室测量的实验数据进行训练和验证的,其中包括 49 个燃料电池的数据,详细说明了其阴极 CL 成分、运行条件、加速应力测试条件、极化曲线和整个寿命期间的 ECSA 测量结果。所介绍的 ANN 模型在预测 PEM 燃料电池寿命开始和结束时的行为方面都表现出了极高的可靠性。这样就能更深入地了解每项输入对性能和耐用性的影响。此外,该模型还能有效地用于优化 CL 成分。本文展示了人工智能与高质量数据库相结合在推动燃料电池研究方面的巨大潜力。
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Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data
The catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each substance within the CL must be meticulously balanced to achieve optimal operation. In this work, we apply an Artificial Neural Network (ANN) model to forecast the performance and durability of a PEM fuel cell based on its cathode CL composition. The model is trained and validated based on experimental data measured at our laboratories, which consist of data from 49 fuel cells, detailing their cathode CL composition, operating conditions, accelerated stress test conditions, polarization curves and ECSA measurements throughout their lifespan. The presented ANN model demonstrates exceptional reliability in predicting PEM fuel cell behavior for both beginning and end of life. This allows for a deeper understanding of the influence of each input on performance and durability. Furthermore, the model can be effectively applied to optimize the CL composition. This paper demonstrates the immense potential of AI, combined with a high-quality database, to advance fuel cell research.
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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