标题： 在退化中适应：通过物理引导的测试时训练进行跨域锂离子电池健康估计 显示英文标题

标题： Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training

摘要： 锂离子电池（LIBs）的健康建模对于安全高效的能源管理至关重要，并具有重要的社会经济影响。 尽管基于机器学习（ML）的状态健康度（SOH）估计方法在准确性方面取得了显著进展，但高质量LIB数据的缺乏仍然是一个主要障碍。 现有的跨领域LIB SOH估计的迁移学习方法显著减轻了目标LIB数据的标注负担，然而，它们仍然需要足够的未标记目标数据（UTD）以有效适应目标领域。 由于退化实验的耗时性，收集这些UTD具有挑战性。 为了解决这个问题，我们引入了一个实用的测试时间训练框架，BatteryTTT，它利用在退化过程中收集的每个UTD持续适应模型，从而显著减少数据收集时间。 为了充分利用每个UTD，BatteryTTT将现代LIB的固有物理规律整合到自监督学习中，称为物理引导的测试时间训练。 此外，我们通过模型重新编程和前缀提示适应评估大型语言模型（LLMs）在电池序列建模中的潜力。 BatteryTTT与LLM建模的结合，称为GPT4Battery，在当前的LIB基准测试中实现了最先进的泛化结果。 此外，我们通过在我们的实际电池管理系统（BMS）中部署它来评估300Ah大容量储能LIBs的实用价值和可扩展性。 显示英文摘要

摘要： Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications. Although Machine Learning (ML)-based State of Health (SOH) estimation methods have made significant progress in accuracy, the scarcity of high-quality LIB data remains a major obstacle. Existing transfer learning methods for cross-domain LIB SOH estimation have significantly alleviated the labeling burden of target LIB data, however, they still require sufficient unlabeled target data (UTD) for effective adaptation to the target domain. Collecting this UTD is challenging due to the time-consuming nature of degradation experiments. To address this issue, we introduce a practical Test-Time Training framework, BatteryTTT, which adapts the model continually using each UTD collected amidst degradation, thereby significantly reducing data collection time. To fully utilize each UTD, BatteryTTT integrates the inherent physical laws of modern LIBs into self-supervised learning, termed Physcics-Guided Test-Time Training. Additionally, we explore the potential of large language models (LLMs) in battery sequence modeling by evaluating their performance in SOH estimation through model reprogramming and prefix prompt adaptation. The combination of BatteryTTT and LLM modeling, termed GPT4Battery, achieves state-of-the-art generalization results across current LIB benchmarks. Furthermore, we demonstrate the practical value and scalability of our approach by deploying it in our real-world battery management system (BMS) for 300Ah large-scale energy storage LIBs.