返回
A physics-aware large language model driven hyper-heuristic optimization framework for parameter identification of solid oxide fuel cell
DOI:10.1016/j.seta.2026.105410.png)
摘要
En 中文
• 提出了一种面向燃料电池参数估计的物理感知框架。
• 导数特征能够实现具有物理可解释性的优化指导。
• 语言模型在优化过程中自适应选择搜索算子。
• 数据完整性被证明对参数可靠性有显著影响。
• 在单电池和电池堆系统中均实现了更高的精度。
Keyword:
Solid oxide fuel cell
Parameter identification
Large language model
Hyper-heuristic
Physics-aware optimization
期刊
IF:
7
论文数:
4.8K
被引数:
2.2W
机构
引用论文
Large language model-based evolutionary optimizer: Reasoning with elitism基于大型语言模型的进化优化器: 精英主义推理
NEUROCOMPUTING
IF6.5
Critical ionic transport across an oxygen-vacancy ordering transition跨氧空位有序过渡的临界离子传输
NATURE COMMUNICATIONS
IF15.7
A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient
APPLIED ENERGY
IF11
A complete polarization model of a solid oxide fuel cell and its sensitivity to the change of cell component thickness固体氧化物燃料电池的完全极化模型及其对电池组件厚度变化的敏感性
Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells
APPLIED ENERGY
IF11
Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm
ENERGY
IF9.4

