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.

翻译：准确预测健康状态（SOH）对于确保锂离子电池的安全可靠运行至关重要。然而，现有模型通常基于特定条件下的实验室测试进行校准，往往难以泛化到因微小制造差异而不同的新电池或不同工况下的电池。为解决这一挑战，本文提出一种考虑不确定性的迁移学习框架，将长短期记忆网络（LSTM）模型与基于最大均值差异（MMD）的域适应方法相结合，并通过共形预测（CP）实现不确定性量化。LSTM模型在旨在捕获电极制造与运行条件中真实世界变异性的虚拟电池数据集上进行训练。MMD通过对齐模拟域与目标域之间的潜在特征分布来缓解域偏移，而CP则提供经过校准、无分布假设的预测区间。该框架提升了跨异构电池的健康状态预测的泛化能力与可信度。