高性能燃料电池的有序气体扩散层通过贝叶斯机器学习的人工智能引导的设计
Jing Sun1, Pengzhu Lin1, Lin Zeng2
1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Nature communications
|July 15, 2025
概括
我们开发了一种机器学习方法来设计更好的气体扩散层 (GDL) 用于质子交换膜燃料电池 (PEMFC). 与传统的试错设计相比,这种方法显著提高了功率密度和限制电流密度.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 质子交换膜燃料电池 (PEMFC) 需要优化的气体扩散层 (GDL) 以提高性能和降低成本.
- 目前的GDL设计依赖于耗时的试错方法,阻碍了快速的进步.
- 开发先进的GDL对于提高能源转换效率和燃料电池技术至关重要.
研究的目的:
- 引入一种新的,闭环贝叶斯式机器学习工作流程,用于合理设计GDL结构.
- 为了加速发现最佳的GDL架构,最大限度地提高PEMFC性能.
- 克服传统的,经验性的GDL开发方法的局限性.
主要方法:
- 利用人工神经网络快速计算重建的GDL的异型运输特性.
- 采用贝叶斯优化算法,有效地搜索最佳的GDL结构.
- 使用受控电技术制造了经过计算设计的GDL结构.
主要成果:
- 贝叶斯优化仅在40个步骤中确定了最佳的GDL结构.
- 最佳的GDL结构具有具有中等直径的高度定向的纤维.
- 使用优化的GDL制造的PEMFC实现了2.17W cm−2的功率密度和约7200 mA cm−2.2的极限电流密度.
结论:
- 拟议的机器学习方法在GDL设计中明显优于传统方法.
- 优化的GDL结构表现出卓越的性能,远远超过商业GDL.
- 这项研究验证了将人工智能驱动的设计与下一代燃料电池的先进制造相结合的有效性.
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