发表机构
Technical University of Munich; STABL Energy GmbH（慕尼黑工业大学; STABL能源有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对梯次利用电池系统，提出基于现场数据结合高斯过程回归重构OCV曲线的框架，可联合估算电池SOH与SOC，发现了电池异质性、SOC不平衡及故障电池，且验证了其优于集中模组模型的估算精度。
AI 中文摘要
电池储能的有效利用依赖于对其健康状态（SOH）和荷电状态（SOC）的可靠估算。基于模型的状态估算需要开路电压（OCV）曲线，而梯次利用电池的OCV曲线通常是未知的。本文提出一种仅通过现场运行数据联合估算等效电路模型的状态与参数的框架，采用高斯过程回归重构OCV曲线。将该框架应用于由27个模组、324个电池组成的真实梯次利用电池系统，结果揭示了电池SOH的异质性、系统性的SOC不平衡以及2个故障电池，所有结果均通过参考测量得到验证。本文还将电池级的SOH和SOC聚合到模组层面，并与未使用单个电池电压拟合的集中模组模型进行对比，该集中模组模型仅能跟踪平均行为，无法捕捉限制电池，对SOH的高估最高达31%，对SOC的高估最高达23%。
英文摘要
Effective use of battery storage depends on reliable estimation of its state of health (SOH) and state of charge (SOC). Model-based state estimation requires the open-circuit voltage (OCV) curve, which is typically unknown for second-life batteries. We present a framework that jointly estimates the states and parameters of an equivalent circuit model solely from field operation data, using Gaussian process regression to reconstruct the OCV curve. Applied to a real second-life battery system of 27 modules and 324 cells, it reveals SOH heterogeneity, a systematic SOC imbalance, and two faulty cells, all validated against a reference measurement. We aggregate the cell SOH and SOC to module level and benchmark them against a lumped-module model fitted without the individual cell voltages. The lumped-module model follows the average behavior and cannot capture the limiting cells, overestimating SOH by up to 31% and SOC by up to 23%.
Comments35 pages, 9 figures, includes supplementary material (10 pages, 9 supplementary figures, 2 supplementary notes). Code and data: https://doi.org/10.5281/zenodo.22281620
