ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning

TL;DR

ACCEPT is a contrastive learning framework that models lithium-ion battery degradation by linking physical parameters to operational data, enabling accurate, zero-shot predictions across various battery types and conditions.

Contribution

This work introduces ACCEPT, a novel contrastive learning-based model that combines physical insights with data-driven methods for improved battery degradation forecasting and generalization.

Findings

Achieves reliable degradation forecasts across multiple battery chemistries.

Enables zero-shot inference for new battery types and conditions.

Outperforms traditional data-driven models in critical accelerated degradation scenarios.

Abstract

Modeling lithium-ion battery (LIB) degradation offers significant cost savings and enhances the safety and reliability of electric vehicles (EVs) and battery energy storage systems (BESS). Whilst data-driven methods have received great attention for forecasting degradation, they often demonstrate limited generalization ability and tend to underperform particularly in critical scenarios involving accelerated degradation, which are crucial to predict accurately. These methods also fail to elucidate the underlying causes of degradation. Alternatively, physical models provide a deeper understanding, but their complex parameters and inherent uncertainties limit their applicability in real-world settings. To this end, we propose a new model - ACCEPT. Our novel framework uses contrastive learning to map the relationship between the underlying physical degradation parameters and observable operational quantities, combining the benefits of both approaches. Furthermore, due to the similarity of degradation paths between LIBs with the same chemistry, this model transfers non-trivially to most downstream tasks, allowing for zero-shot inference. Additionally, since categorical features can be included in the model, it can generalize to other LIB chemistries. This work establishes a foundational battery degradation model, providing reliable forecasts across a range of battery types and operating conditions.