Physics-guided TL-LSTM network for early-stage degradation trajectory prediction of lithium-ion batteries

Abstract

Predicting early degradation trajectories of lithium-ion batteries is crucial in enhancing system reliability and promoting battery technology advancement. Existing data-driven methods often require large amounts of historical data and similar cycle information, which are not easily accessible in real-world applications. To this end, a physics-guided TL-LSTM network is proposed for degradation trajectory prediction, which integrates the physical degradation property into a transfer learning framework. Specifically, we first build a backbone network that combines a Long Short-Term Memory (LSTM) module and a Fully Connected (FC) module to perform fine-grained model training on the source domain. Then, the well-trained LSTM module is frozen and transferred to the target domain, and the FC module is fine-tuned using 30% of its early-cycle data. The entire network optimization process is guided by two inherent properties of battery degradation. Extensive comparative analysis demonstrates that our proposed approach outperforms existing methods in predicting early degradation trajectories.