# BatteryMFormer： 面向电池退化轨迹预测的多层级学习

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-26 08:00
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmpppl0wm027sslvyw6a4dm9i
- 原文链接：https://arxiv.org/abs/2605.27044

## AI 摘要

BatteryMFormer是一个用于早期电池退化轨迹预测的多层级Transformer模型。该模型通过老化条件感知解码器、元退化模式记忆库以及联合捕捉时间动态与SOC区间变化的双视角编码器，显式建模电池退化数据的多层级结构与局部变化特征。在四个电池领域的实验中，其预测性能持续优于最先进的基线方法。模型代码已开源。

## 正文

Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization, manufacturing, and deployment. Battery degradation data exhibit two key characteristics. First, degradation data present a multi-level structure, including regularities shared within aging conditions and trajectory patterns shared across batteries. Second, degradation-related variations in voltage-current profiles are often localized to specific state-of-charge (SOC) intervals. Existing approaches often fail to explicitly model these characteristics. To bridge this gap, we propose BatteryMFormer, a multi-level Transformer for early BDTF. BatteryMFormer integrates (1) an aging-condition-aware decoder that injects aging-condition priors via aging-condition-informed queries and aging-condition-aware attention, (2) a meta degradation pattern memory that learns and retrieves trajectory prototypes to guide long-horizon forecasting, and (3) a dual-view encoder that jointly captures temporal dynamics and SOC-localized variations from voltage and current time series. Extensive experiments on four battery domains show that BatteryMFormer consistently outperforms state-of-the-art baselines, marking a significant step toward reliable BDTF. Our code is available at https://github.com/Ruifeng-Tan/BatteryMFormer.