Unlocking Interpretable Prediction of Battery Random Discharge Capacity With Domain Adaptative Physics Constraint

Abstract

Tracking the battery discharge capacity is significant, yet challenging due to complicated degradation patterns as well as varying or even random usage scenarios. This work proposes a physics-constrained domain adaptation framework to predict the capacities during random discharge with non-destructive mechanism diagnosis using early or random discharging information. By imposing the impedance as physical constraints in a domain adaptative layer, the interpretability and generalization of the model can be improved as the physics-constrained layer provides physical insights into the battery mechanism characteristics, enabling onboard and non-destructive diagnosis without complex tests. The learned impedances in the physics-constrained layer are well-fitted to the real ones, suggesting accurate physical insights and, therefore, good interpretability of the trained model. Furthermore, apart from capacity prediction, the aging mechanisms of the cell can be interpreted through the learned physics from this deep learning framework without impedance measurement. Such interpretation has also been validated experimentally through post-mortem analysis. This work provides an example of grey-box modeling of complex dynamic systems where deep learning models can provide certain physical details to increase the model's interpretability.