Deep learning design of functionally graded porous electrode of proton exchange membrane fuel cells
Deep learning design of functionally graded porous electrode of proton exchange membrane fuel cells
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DOI：
10.1016/j.energy.2023.128463
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发表时间：
2023-07
期刊：
影响因子：
9
通讯作者：
X. Y. Tai;Lei Xing;S. Christie;Jin Xuan
中科院分区：
文献类型：
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作者：
X. Y. Tai;Lei Xing;S. Christie;Jin Xuan
对于下一代质子交换膜（PEM）燃料电池而言，功能梯度电极取代传统的功能组分均匀分布的电极，具有突出的性能、效率和避免加剧催化剂成本的优点。由于PEM燃料电池系统的复杂和非线性行为，需要快速有效的计算模型和优化算法来处理电极设计参数和电池性能之间的这种复杂关系。在这项工作中，开发了一个具有多方向梯度电极的多物理模型，其中嵌入了深度机器学习方法，以创建由非支配排序遗传算法（NSGA-II）授权的多目标优化的代理模型。通过训练获得了均方误差低于0.01的鲁棒预测深度神经网络模型，然后与NSGA-II耦合，以评估和优化燃料电池的性能和成本。值得注意的是，帕累托前沿成功地定义了目标之间的权衡关系，它有助于识别最佳点，在该点上，它满足成本效益，同时保持相对较高的电池性能。我们的工作提出了一个有前途的策略，以优化燃料电池系统与潜在的相互作用，并允许快速，准确的预测和优化。
For the next generation of proton exchange membrane (PEM) fuel cells, the conventional electrode with uniform distribution of functional components is urged to be replaced by functional graded electrode for the prominent performance, efficiency and avoid exacerbated catalyst cost. Due to the complex and non-linear behaviours of PEM fuel cell system, rapid and effective computational model and optimisation algorithm are required to handle such a complex relationship between electrode design parameters and cell performance. In this work, a multi-physics model with multi-directionally graded electrode is developed, in which a deep machine learning approach is embedded, to create a surrogate model for multi-objective optimisation empowered by non-dominated sort genetic algorithm (NSGA-II). A robust prediction deep neural network model with the mean square error lower than 0.01 is obtained from training and then coupling with NSGA-II to evaluate and optimise the fuel cell performances and cost. Remarkably, the Pareto front is successfully defining the trade-off relationship between the objectives where it aids to identify an optimum point where it satisfies the cost effectiveness while maintaining relatively high cell performances. Our work presents a promising strategy to optimise the fuel cell system with underlying interaction and allow rapid and accurate prediction and optimisation.