Maximizing power density in proton exchange membrane fuel cells: An integrated optimization framework coupling multi-physics structure models, machine learning, and improved gray wolf optimizer

This study proposes an innovative optimization framework to optimize channel structure and maximize power density by coupling multi-physics structure models, machine learning, and swarm intelligence algorithms. First, proton exchange membrane fuel cells (PEMFC) imitated water-drop block channels are employed for constructing multi-physics structure models. A database of the PEMFC output performance under various structural parameters of imitated water-drop block is established. Then, a machine-learning-based surrogate model is constructed based on the adaptive boosting (AdaBoost) ensemble algorithm to predict the output performance under different channel parameters. Finally, the improved gray wolf optimizer (IGWO) fitness function is calculated using a surrogate model to establish an optimization framework for effectively optimizing the channel structure. Results show that the AdaBoost ensemble surrogate model predicts the PEMFC polarization curves with extremely high accuracy and efficiency within one second. The optimization framework is capable of swiftly predicting both the optimal channel structure and maximum power density in under two minutes. The predicted values are returned to the physical model for validation with an error of only 3.96%. Simultaneously, the optimal channel structure can effectively enhance the PEMFC performance. The proposed optimization framework will guide the performance prediction and channel structure multivariate optimization.

Automated summary

This study presents an innovative optimization framework that combines multi-physics structure models, machine learning, and swarm intelligence algorithms to optimize channel structure and maximize power density. Proton exchange membrane fuel cells (PEMFC) with imitated water-drop block channels are used to construct the multi-physics structure models. A database of PEMFC output performance under different structural parameters is established. A machine-learning-based surrogate model is constructed using the AdaBoost ensemble algorithm to predict the output performance under different channel parameters. The improved gray wolf optimizer fitness function is calculated using the surrogate model to establish an optimization framework for effectively optimizing the channel structure. Results show that the AdaBoost ensemble surrogate model accurately predicts the PEMFC polarization curves within one second. The optimization framework can swiftly predict the optimal channel structure and maximum power density in under two minutes. The proposed framework will guide performance prediction and multivariate optimization of channel structure.