Modelling Anti-Corrosion Coating Performance of Metallic Bipolar Plates for PEM Fuel Cells: A Machine Learning Approach

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-06-26 DOI:10.1016/j.egyai.2024.100391
Pramoth Varsan Madhavan , Samaneh Shahgaldi , Xianguo Li
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Abstract

Proton exchange membrane (PEM) fuel cells have significant potential for clean power generation, yet challenges remain in enhancing their performance, durability, and cost-effectiveness, particularly concerning metallic bipolar plates, which are pivotal for lightweight compact fuel cell stacks. Protective coatings are commonly employed to combat metallic bipolar plate corrosion and enhance water management within stacks. Conventional methods for predicting coating performance in terms of corrosion resistance involve complex physical-electrochemical modelling and extensive experimentation, with significant time and cost. In this study machine learning techniques are employed to model metallic bipolar plate coating performance, diamond-like-carbon coatings of varying thicknesses deposited on SS316L are considered, and coating performance is evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy. The obtained experimental data is split into two datasets for machine learning modelling: one predicting corrosion current density and another predicting impedance parameters. Machine learning models, including extreme gradient boosting (XGB) and artificial neural networks (ANN), are developed, and optimized to predict coating performance attributes. Data preprocessing and hyperparameter tuning are carried out to enhance model accuracy. Results show that ANN outperforms XGB in predicting corrosion current density, achieving an R2 > 0.98, and accurately predicting impedance parameters with an R2 > 0.99, indicating that the models developed are very promising for accurate prediction of the corrosion performance of coated metallic bipolar plates for PEM fuel cells.

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PEM 燃料电池金属双极板防腐蚀涂层性能建模:机器学习方法
质子交换膜(PEM)燃料电池在清洁发电方面具有巨大潜力,但在提高其性能、耐用性和成本效益方面仍面临挑战,尤其是对轻质紧凑型燃料电池堆至关重要的金属双极板。通常采用保护涂层来防止金属双极板腐蚀并加强堆内的水管理。预测涂层耐腐蚀性能的传统方法涉及复杂的物理-电化学建模和大量实验,耗费大量时间和成本。本研究采用机器学习技术建立金属双极板涂层性能模型,考虑在 SS316L 上沉积不同厚度的类金刚石碳涂层,并使用电位极化和电化学阻抗光谱评估涂层性能。获得的实验数据被分成两个数据集用于机器学习建模:一个数据集预测腐蚀电流密度,另一个数据集预测阻抗参数。开发并优化了机器学习模型,包括极端梯度提升(XGB)和人工神经网络(ANN),以预测涂层性能属性。通过数据预处理和超参数调整来提高模型的准确性。结果表明,人工神经网络在预测腐蚀电流密度方面优于 XGB,R2 为 0.98,并能准确预测阻抗参数,R2 为 0.99,这表明所开发的模型在准确预测 PEM 燃料电池涂层金属双极板的腐蚀性能方面大有可为。
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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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