BatteryMFormer：用於電池退化軌跡預測的多層級學習

摘要

電池早期退化軌跡預測（BDTF）旨在從早期運轉數據中預測全生命週期的健康狀態軌跡，對於電池優化、製造與部署至關重要。電池退化數據呈現兩項關鍵特徵：首先，退化資料具有多層次結構，包含老化條件下的共同規律以及跨電池共享的軌跡模式；其次，電壓-電流曲線中與退化相關的變化常侷限於特定充電狀態（SOC）區間。現有方法通常未能明確建模這些特徵。為解決此問題，我們提出BatteryMFormer——一種用於早期BDTF的多層次Transformer模型。BatteryMFormer整合了（1）老化條件感知解碼器：透過老化條件查詢注入先驗知識，並採用老化條件感知注意力機制；（2）元退化模式記憶體：學習與檢索軌跡原型以引導長期預測；（3）雙視角編碼器：同時捕捉電壓與電流時間序列中的時間動態與SOC局部變化。在四個電池領域的廣泛實驗中，BatteryMFormer持續超越當前最優基準方法，為實現可靠BDTF邁出重要一步。我們的程式碼已公開於 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.