用迁移学习+不确定性量化，提升电池健康状态预测的泛化能力。
Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability
- 结合LSTM与最大均值差异对齐领域特征，减少制造和使用差异影响。
- 在虚拟电池数据集上训练，实现跨细胞的高精度健康状态预测。
- 通过置信区间校准，让预测结果更可信，适合工业场景应用。
准确预测锂离子电池的健康状态（SOH）对于保障其安全可靠运行至关重要。然而，现有模型通常在特定实验条件下标定，难以泛化到因微小制造差异或使用条件不同而产生的新电池。为此，提出一种不确定性感知的迁移学习框架，结合长短期记忆（LSTM）模型、基于最大均值差异（MMD）的领域自适应以及通过分位数回归的置信区间校准（Conformal Prediction, CP）。LSTM模型在设计的虚拟电池数据集上训练，以捕捉电极制造和运行条件的真实世界变异性。MMD用于对齐模拟域与目标域的潜在特征分布，缓解领域偏移问题；CP提供无需分布假设的校准预测区间。该框架显著提升了不同电池间的泛化性能与预测可信度。
原文摘要 · Abstract (English)
Accurate forecasting of state-of-health (SOH) is essential for ensuring safe and reliable operation of lithium-ion cells. However, existing models calibrated on laboratory tests at specific conditions often fail to generalize to new cells that differ due to small manufacturing variations or operate under different conditions. To address this challenge, an uncertainty-aware transfer learning framework is proposed, combining a Long Short-Term Memory (LSTM) model with domain adaptation via Maximum Mean Discrepancy (MMD) and uncertainty quantification through Conformal Prediction (CP). The LSTM model is trained on a virtual battery dataset designed to capture real-world variability in electrode manufacturing and operating conditions. MMD aligns latent feature distributions between simulated and target domains to mitigate domain shift, while CP provides calibrated, distribution-free prediction intervals. This framework improves both the generalization and trustworthiness of SOH forecasts across heterogeneous cells.
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