DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis

TL;DR

A novel general-purpose model for battery degradation prediction and synthesis, DiffBatt is introduced, leveraging an innovative combination of conditional and unconditional diffusion models with classifier-free guidance and transformer architecture that achieves high expressivity and scalability.

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.

Figures (6)

• Figure 1: Schematic view of the model architecture. Adapted and modified from the work by Frrutter2024, with permission from the authors. Modifications include context-specific changes.
• Figure 2: Denoising steps for one test sample of the MATR dataset.
• Figure 3: Results obtained from DiffBatt for RUL prediction and SOH estimation on the MIX test datasets. (a) Predicted RUL against the reference, colored by the standard deviation $\sigma_{\rm RUL}$. (b) Generated samples and selected predictions based on the best fit to the first 100 cycles, compared against the reference SOH for test samples with the lowest (left) and highest (right) uncertainty in the predictions.
• Figure 4: Train (up) and test (bottom) samples for each cell chemistry. The data is scaled using the SOH of the first cycle.
• Figure 5: SOH predictions against reference for all the test samples of MIX dataset. The pink dashed line shows the prediction and the cyan solid line shows the reference.