DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis
This work addresses battery degradation prediction for green technology applications, representing an incremental advance with a novel method for a known bottleneck.
The paper tackled the problem of accurately predicting battery degradation, a critical challenge for sustainable energy, by introducing DiffBatt, a diffusion-based model that achieved a mean RMSE of 196 cycles in remaining useful life prediction, outperforming other models and enabling synthetic data generation.
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.