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Robust and Reliable Early-Stage Lifetime Prediction of Batteries via Transferable One-Shot Learning
编号：88 访问权限：仅限参会人 更新：2025-11-10 12:17:55 浏览：100次 口头报告

报告开始：2025年11月23日 08:30（Asia/Shanghai）

报告时间：20min

所在会场：[S3] Parallel Session 3 [S3-2] Parallel Session 3-23 AM

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摘要
Accurate early-stage lifetime prediction of lithium-ion batteries is essential for their optimised utilisation and accelerated product development. However, obtaining full-cycle degradation data under normal usage conditions is both costly and time-consuming. Accelerated ageing tests offer a practical alternative, but a key challenge remains: how to generalise models trained on such data to normally aged batteries—an out-of-distribution (OOD) problem common in real-world applications. To address this, we propose a transferable one-shot diagnostic framework that requires only limited lifetime labels from short-life cells (averaging ~484 cycles) subjected to accelerated ageing, along with a single long-life cell (>1200 cycles) under normal conditions, to facilitate accurate predictions across the long-life population. Validation results show that the proposed model generalises effectively to long-life cells exceeding 800 cycles (with an average lifespan of 1147 cycles and a maximum of 2227 cycles), achieving an average prediction error below 15% across 10 independent runs of model training and testing. Compared to conventional baselines, the framework yields a substantial accuracy improvement of up to 68%, demonstrating its efficacy for robust and reliable early-stage lifetime prediction under OOD conditions.
 
关键词
Lithium-ion batteries, life prediction, early life, one-shot learning, accelerated ageing test, variational autoencoder
报告人
Ruohan Guo
Postdoc The Hong Kong Polytechnic University

稿件作者
Ruohan Guo The Hong Kong Polytechnic University
Jinpeng Tian The Hong Kong Polytechnic University
Dandan Peng The Hong Kong Polytechnic University
Chi-yung Chung The Hong Kong Polytechnic University
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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