发表机构
Technische Universität Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对标签稀疏下锂离子电池SOH估计问题，提出基于CNN-GRU的退化对齐SSL框架，利用排序预训练结合微调，仅用1%标注数据即可实现高精度SOH估计，为实际应用提供新方向。
AI 中文摘要
准确估计健康状态（SOH）是保障电池系统安全、优化使用的基础。尽管数据驱动的SOH估计模型效果显著，但这类模型通常需要大量高质量的带标注循环数据，而实际场景中这类标注数据的数量和覆盖范围往往十分稀疏。因此，本研究提出一种基于卷积神经网络-门控循环单元（CNN-GRU）模型的退化对齐自监督学习（SSL）框架，该框架通过循环排序目标作为预训练的 pretext 任务，从未标注数据中学习与老化过程一致的表征，从而在稀疏标注数据上进行微调后实现鲁棒的SOH估计。测试结果表明，所提出的基于排序的SSL方法能为预训练模型赋予来自未标注数据的退化对齐信息，微调后模型可实现准确、鲁棒的SOH估计，即便仅使用占比1%、分布不均的有限标注训练数据，在测试电池上仍能达到1.718%的平均绝对误差（MAE）和2.329%的均方根误差（RMSE）。此外，本研究还深入分析了电池退化数据的标注分布所产生的影响。我们认为，该研究可为实际应用中标签稀疏场景下的锂离子电池SOH估计提供新的思路。
英文摘要
An accurate estimation of the state of health (SOH) underpins safe and optimized use of the battery system. Although compelling, data-driven SOH estimation models typically require large amounts of high-quality labeled cycling data, while in practice such labels are often sparse in both quantity and coverage. Therefore, in this work, we propose a degradation-aligned self-supervised learning (SSL) framework based on a convolutional neural network-gated recurrent unit (CNN-GRU) model, which learns aging-consistent representations from unlabeled data through a cycle-order ranking objective as the pretext task for pretraining, thereby enabling robust SOH estimation after fine-tuning on sparsely labeled data. Test results showcase that the proposed ranking-based SSL approach proves to endow the pretrained model with degradation awareness from unlabeled data, and after fine-tuning the model can carry out accurate, robust SOH estimation, even when only an extremely limited amount of 1% of unevenly distributed labeled training data is available, where the MAE of 1.718% and RMSE of 2.329% can be achieved on the test cell. In addition, in-depth analyses are presented regarding the influences of label distribution and cross-cell robustness. We believe this work could shed new light on label-efficient SOH estimation of lithium-ion batteries, addressing a practical need in battery management.
CommentsPublished version. This article is published open access under the Creative Commons Attribution 4.0 International License. The final published version is available at [Energy and AI] via DOI: 10.1016/j.egyai.2026.100884
Journal refEnergy and AI, vol. 26, p. 100884, Dec. 2026
DOI：10.1016/j.egyai.2026.100884
