发表机构
Tecnalia - Basque Research Technology Alliance (BRTA); Mondragon Unibertsitatea（泰克纳利亚——巴斯克研究与技术联盟（BRTA）; 蒙德拉贡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出物理信息深度学习方法，利用任意电压区间的部分电池放电数据，实现低于4% MAPE的实时SOH估计与无先验知识的退化趋势预测，克服了传统方法的局限。
AI 中文摘要
随着可再生能源的并网规模不断扩大，储能系统变得至关重要，因此准确估计其健康状态（State of Health, SOH）及退化行为具有关键意义。本研究提出一种用于锂离子电池SOH预测的物理信息深度学习方法，该方法利用从任意电压区间提取的不完整放电曲线，可反映真实且异构的运行条件。所提方法将数据驱动学习与受物理规律约束的退化动力学相结合，以确保从部分放电信息中获得一致且可靠的SOH估计，其平均绝对百分比误差（MAPE）低于4%。此外，引入了一种实时退化趋势估计策略，无需先验知识或历史数据即可检测关键老化转变，适用于各类电池。总体而言，该方法可实现从任意放电片段进行SOH估计，并生成持续整合所有使用情况的实时退化预测，克服了以往方法依赖固定协议或早期非自适应预测的局限。
英文摘要
With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical. In this work, we propose a physics-informed deep learning approach for lithium-ion battery SOH prediction using incomplete discharge curves extracted from arbitrary voltage ranges, thereby reflecting realistic and heterogeneous operating conditions. The proposed method combines data-driven learning with physically motivated degradation dynamics to ensure consistent and reliable SOH estimation from partial discharge information, achieving a MAPE below 4$\%$. In addition, a real-time degradation trend estimation strategy is introduced to detect key aging transitions without requiring prior knowledge or historical data, making it applicable to a wide range of batteries. Overall, our approach enables SOH estimation from arbitrary discharge segments and a real-time degradation forecast that continuously integrates all usage, overcoming previous methods that rely on fixed protocols or early, non-adaptive predictions.
