BatteryMFormer: 面向电池退化轨迹预测的多层级学习

摘要

早期电池退化轨迹预测（BDTF）旨在从早期运行数据中预测全生命周期健康状态轨迹，这对电池优化、制造与部署至关重要。电池退化数据呈现两个关键特征：首先，退化数据具有多层次结构，既包含老化条件下的共性规律，又涵盖跨电池共享的轨迹模式；其次，电压-电流曲线中与退化相关的波动往往集中在特定的荷电状态（SOC）区间。现有方法通常未能显式建模这些特征。为解决这一局限，我们提出BatteryMFormer——一种用于早期BDTF的多层次Transformer模型。BatteryMFormer整合了：（1）老化条件感知解码器，通过老化条件引导的查询及老化条件感知注意力机制注入先验知识；（2）元退化模式记忆模块，学习并检索轨迹原型以指导长期预测；（3）双视角编码器，从电压和电流时间序列中联合捕获时序动态及SOC局部变化。在四个电池领域的广泛实验表明，BatteryMFormer持续优于当前最优基线，标志着向可靠BDTF迈出重要一步。我们的代码已开源：https://github.com/Ruifeng-Tan/BatteryMFormer。

English

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.