通过物理启发的特征设计，仅用形成期数据实现高精度电池寿命预测。
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation
- 基于物理机制设计可解释特征，无需额外测试周期。
- 仅用两个电压特征即达9.20%中位误差，优于数千个自动特征模型。
- 适合电池研发与制造优化，兼顾精度与可解释性。
锂离子电池制造中的形成阶段优化因固态电解质界面形成机理不明确，且寿命测试需约100天而面临挑战。本文提出一种系统化特征设计框架，可在极少领域知识下实现形成期内的准确循环寿命预测。从形成数据中提取的两个简单Q(V)特征，无需额外诊断周期，实现了9.20%的中位预测误差，优于使用预定义特征的数千个自动机器学习模型。我们归因于所设计特征的物理根源——框架识别的电压范围捕捉了形成温度与微观颗粒电阻异质性的影响。该方法通过数据驱动特征设计与机理理解的结合，加速形成研究进程。
原文摘要 · Abstract (English)
Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins - the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.
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