Interpretable Deep Learning Using Temporal Transformers for Battery Degradation Prediction - James Sadler; Rizwaan Mohammed; Kotub Uddin (Batteries, 2025)
Accurate modelling of lithium-ion battery degradation is a complex problem, dependent on multiple internal mechanisms that can be affected by a multitude of external conditions. In this study, a transformer-based approach, capable of leveraging historical conditions and known-future inputs is introduced. The model can make predictions from as few as 100 input cycles, and compared to other state-of-the-art techniques, our approach shows an increase in accuracy. The model utilises specialised components within its architecture to provide interpretable results, introducing the possibility of understanding path-dependency in Li-Ion battery degradation. The ability to incorporate static metadata opens the door for a foundational deep learning model for battery degradation forecasting.