DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis

TL;DR

DiffBatt is a novel diffusion-based model that accurately predicts battery degradation and synthesizes degradation data, improving prediction accuracy and enabling data augmentation for sustainable energy applications.

Contribution

The paper introduces DiffBatt, a new diffusion model combining transformer architecture for battery degradation prediction and synthesis, advancing accuracy and generalizability.

Findings

Achieves a mean RMSE of 196 cycles in remaining useful life prediction.

Outperforms existing models in prediction accuracy.

Enables generation of synthetic degradation curves for data augmentation.

Abstract

Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery capacity loss accurately remains a formidable task due to its complex nature, influenced by both aging and cycling behaviors. To address this challenge, we introduce a novel general-purpose model for battery degradation prediction and synthesis, DiffBatt. Leveraging an innovative combination of conditional and unconditional diffusion models with classifier-free guidance and transformer architecture, DiffBatt achieves high expressivity and scalability. DiffBatt operates as a probabilistic model to capture uncertainty in aging behaviors and a generative model to simulate battery degradation. The performance of the model excels in prediction tasks while also enabling the generation of synthetic degradation curves, facilitating enhanced model training by data augmentation. In the remaining useful life prediction task, DiffBatt provides accurate results with a mean RMSE of 196 cycles across all datasets, outperforming all other models and demonstrating superior generalizability. This work represents an important step towards developing foundational models for battery degradation.