Data-Driven Cross-Condition Optimization of Membrane-Electrode Assembly Performance of Proton Exchange Membrane Fuel Cells

LI Mingzhe; YANG Liu; FU Kaihao; LI Ping; CAO Chenxi

doi:10.11949/0438-1157.20251355

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Data-Driven Cross-Condition Optimization of Membrane-Electrode Assembly Performance of Proton Exchange Membrane Fuel Cells

更新时间：2026-04-24

- Data-Driven Cross-Condition Optimization of Membrane-Electrode Assembly Performance of Proton Exchange Membrane Fuel Cells
- CIESC Journal Pages: 1-20(2026)
- 作者机构：
  
  1.华东理工大学能源化工过程智能制造教育部重点实验室，上海 200237
  2.华东理工大学化工学院，绿色化工与工业催化全国重点实验室，上海 200237
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20251355
  CLC： TM 911.4
- Received：30 November 2025，
  
  Revised：2026-02-05，
  
  Online First：24 April 2026，
- 稿件说明：
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李明哲, 杨柳, 付凯豪, 李平, 曹晨熙. 数据驱动的质子交换膜燃料电池膜电极性能跨工况优化[J]. 化工学报,

LI Mingzhe, YANG Liu, FU Kaihao, LI Ping, CAO Chenxi. Data-Driven Cross-Condition Optimization of Membrane-Electrode Assembly Performance of Proton Exchange Membrane Fuel Cells[J]. CIESC Journal,
李明哲, 杨柳, 付凯豪, 李平, 曹晨熙. 数据驱动的质子交换膜燃料电池膜电极性能跨工况优化[J]. 化工学报, DOI： 10.11949/0438-1157.20251355

LI Mingzhe, YANG Liu, FU Kaihao, LI Ping, CAO Chenxi. Data-Driven Cross-Condition Optimization of Membrane-Electrode Assembly Performance of Proton Exchange Membrane Fuel Cells[J]. CIESC Journal, DOI： 10.11949/0438-1157.20251355

摘要

质子交换膜燃料电池（Proton Exchange Membrane Fuel Cell， PEMFC）低温适应性问题严重制约了其大范围商业应用。膜电极性质是决定PEMFC冷启动性能的根本因素，然而针对冷启动调优膜电极往往导致常规工况性能下降。本文提出了一种数据驱动的PEMFC膜电极跨工况优化设计框架，对阴、阳极催化层的关键组成和结构参数采样构建高保真机理数据集，训练了基于支持向量机、高斯过程回归、径向基神经网络等方法的代理模型，对冷启动性能关键指标的预测

均值达0.968，同时在常温性能预测中表现出很强的泛化能力。结合AI（Artificial Intelligence）代理模型与NSGA-III（Third-generation Non-dominated Sorting Genetic Algorithm， NSGA-III）算法有效权衡膜电极的跨

工况性能，优化的膜电极性质参数可提升PEMFC冷启动持续时长29.6%、最大温升11.9%，或在维持低温耐受性下提升常规工况最大功率密度3.6%，为PEMFC膜电极的高效理性设计及快速产品迭代提供了重要的理论依据与技术支持。

Abstract

The low-temperature adaptability of proton exchange membrane fuel cells (PEMFC) severely restricts their widespread commercial application. The properties of the membrane electrode assembly (MEA) are the fundamental factors determining the cold start performance of PEMFCs. However

optimizing the MEA for cold start often leads to a decline in performance under conventional conditions. This paper proposes a data-driven cross-condition optimization design framework for MEA of PEMFCs. It involves sampling the key composition and structural parameters of the anode and cathode catalyst layers to construct a high-fidelity mechanistic dataset. Surrogate models based on methods such as support vector machines

Gaussian process regression

and radial basis function neural networks were trained. The mean

of predictions for key cold start performance indicators reached 0.968

while also demonstrating strong generalization capabilities in predicting conventional temperature performance. By integrating the AI surrogate model with the NSGA-III algorithm

the cross-condition performance of the MEA is effectively balanced. Optimized MEA property parameters can enhance the PEMFC cold start duration by 29.6% and the maximum temperature rise by 11.9%. Alternatively

maintaining low-temperature tolerance while increasing the maximum power density under conventional conditions by 3.6%

providing crucial theoretical basis and technical support for efficient rational design and rapid product iteration of MEA of PEMFCs.

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references

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