Transfer Learning for Battery Health Forecasting: From Lab to Real World Data

Abstract

Accurately forecasting lithium-ion battery health in electric vehicles remains challenging due to the scarcity and variability of real-world data and the disconnect between controlled laboratory tests and in-service operation. To address this, a transfer-learning framework is proposed, that leverages diverse lab datasets and small amounts of vehicle-specific data to produce personalized State of Health (SOH) and Remaining Useful Life (RUL) forecasts. The proposed method employs a dual LSTM architecture, where one branch ingests historical SOH trajectories, while a parallel branch processes simple statistical descriptors (mean and standard deviation of voltage, current, and temperature) per cycle. The outputs of the two LSTMs are concatenated and passed through a lightweight MLP to yield cycle-wise forecasts. Models were trained on three open-source lab datasets (MIT, XJTU, HKUST) encompassing varied chemistries and cycling protocols, then evaluated both on a held-out lab domain and on real-world EV data from nine vehicles spanning 18–30 months of operation. Results demonstrate that the dual LSTM consistently outperforms simpler baselines, with fine-tuning on early-life data yielding substantial accuracy gains. Our framework thus effectively provides a step towards bridging the lab-to-road gap, enabling scalable, adaptive battery management.