用扩散模型预测电池衰减，还能生成仿真数据。
DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis
- 结合扩散模型与Transformer，实现衰减预测与生成。
- 剩余寿命预测均方根误差196次循环，优于现有方法。
- 适合电池健康状态评估与数据增强场景。
电池衰减是绿色科技与可持续能源解决方案中的关键挑战。尽管研究众多，由于老化与循环行为的复杂性，准确预测电池容量衰减仍具难度。为此，我们提出通用型电池衰减预测与合成模型DiffBatt。该模型融合条件与无条件扩散模型、无需分类器引导及Transformer架构，具备高表达力与可扩展性。DiffBatt作为概率模型捕捉老化行为不确定性，同时作为生成模型模拟电池衰减过程。在剩余使用寿命预测任务中，模型在所有数据集上均达到均方根误差196次循环，优于所有对比模型，展现出卓越泛化能力。本工作为构建电池衰减基础模型迈出重要一步。
原文摘要 · Abstract (English)
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.
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