AI 中文总结
本文针对基于图的能源系统模型的局限性，提出两种扩展方案，将其应用于36个并联电池组成的锂离子电池模块，温度预测误差低于1°C，验证了该框架的有效性与应用潜力。
AI 中文摘要
基于图的模型已被证明能为复杂多领域能源系统提供结构化表示，但当边功率流依赖于非相邻状态，或单条边承载由不同输入驱动的多种功率流类型时，这类模型存在局限性。本文提出两种通用扩展方案以解决这些问题：一是递归状态-输入反馈方案，可在不改变图结构的前提下将非相邻状态依赖嵌入边输入；二是并行边分解方法，可通过单输入边集表示复合交互，同时保持顶点处的能量守恒。该扩展框架在由36个并联电池组成的锂离子电池模块上进行了验证，所得模型预测模块温度的误差低于1°C。对该电热电池系统的验证表明，该扩展框架适用于无法用现有基于图的公式表示的多领域系统，且在控制与设计研究中具有更广泛应用于复杂能源系统的潜力。
英文摘要
Graph-based models have been shown to provide a structured representation for complex multi-domain energy systems but face limitations when edge power flows depend on non-adjacent states or when a single edge carries multiple power-flow types driven by different inputs. This paper proposes two general extensions to address these limitations: a recursive state-to-input feedback scheme that embeds non-adjacent state dependencies into edge inputs without altering the graph structure, and a parallel edge decomposition method that represents composite interactions using sets of single-input edges while preserving energy conservation at the vertices. The extended framework is demonstrated on a lithium-ion battery module consisting of 36 parallel cells, and the resulting model predicts module temperatures with errors below 1°C. Validation on this electro-thermal battery system demonstrates the effectiveness of the extended framework for multi-domain systems that cannot be represented by previously established graph-based formulations, and indicates its potential for broader application to complex energy systems in control and design studies.
CommentsThis work has been accepted to IFAC World Congress 2026 for publication. 6 pages, 6 figures
