用早期内阻数据提升跨厂电池寿命预测精度
Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers
- 融合电压容量与早期直流内阻数据建模
- 跨厂商预测误差仅150循环，泛化性强
- 适合电池研发与量产企业快速验证设计
锂离子电池在不同制造商间寿命预测面临挑战，源于电极材料、制造工艺和电池结构差异，以及普遍缺乏通用数据。仅基于电压-容量曲线特征的方法难以跨化学体系泛化。本文提出结合传统电压-容量特征与直流内阻（DCIR）测量的新方法，利用早期循环的DCIR数据捕捉内阻增长等关键退化机制，显著提升模型鲁棒性。模型在未见过的制造商、不同电极组成的电池上实现平均绝对误差（MAE）为150循环的寿命预测。该方法减少对新数据收集与重训练的需求，使厂商可基于现有数据优化新电池设计。此外，本文发布了一个新型兼容DCIR的循环数据集，助力电池材料研发生态发展。
原文摘要 · Abstract (English)
Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available data. Methods that construct features solely on voltage-capacity profile data typically fail to generalize across cell chemistries. This study introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements, enabling more accurate and generalizable EOL predictions. The use of early-cycle DCIR data captures critical degradation mechanisms related to internal resistance growth, enhancing model robustness. Models are shown to successfully predict the number of cycles to EOL for unseen manufacturers of varied electrode composition with a mean absolute error (MAE) of 150 cycles. This cross-manufacturer generalizability reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. Additionally, a novel DCIR-compatible dataset is released as part of ongoing efforts to enrich the growing ecosystem of cycling data and accelerate battery materials development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。