This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application.

中文翻译：

---

用于识别锂离子电池老化条件的无监督神经网络

本文探索了一种基于数据驱动方法的新方法，用于识别和跟踪锂离子电池的退化过程。我们的目标是研究是否有可能区分在整体参数（类似的剩余容量、健康状态或内阻）方面呈现相似老化但具有不同应用或使用条件的电池的降解状态（不同的放电电流、放电深度、温度等）。为此，本研究建议使用无监督神经网络自组织图 (SOM) 分析在循环测试中获得的电池的电压波形。在这项工作中，使用真实锂离子电池的实验室数据集，SOM 算法处理电池特征，从而实现电池的智能传感。