用统一模型搞定多种电池管理任务，省数据还省人力。
Multitask Battery Management with Flexible Pretraining
- 用可处理缺失数据的掩码自编码框架学习电池通用特征。
- 在11个数据集上5项任务表现超越专用模型，寿命预测仅需1/50数据。
- 适合需要少样本、跨任务部署的工业电池管理系统。
工业级电池管理涉及多种任务，如状态估计、寿命预测和系统诊断，不同任务使用不同时间尺度、传感器分辨率和数据通道的数据。构建专用方法需大量数据与工程投入，限制了智能电池管理的可扩展性。本文提出灵活掩码自编码器（FMAE），一种能处理缺失数据通道并捕捉片段间关联的预训练框架。FMAE从异构数据中学习统一电池表征，可被各类任务以极小数据与工程成本复用。实验表明，FMAE在五个电池管理任务、十一个电池数据集上持续优于所有专用方法。在剩余寿命预测任务中，仅需50倍少的推理数据即可保持最先进性能。当真实数据缺少系统电压等信息时，FMAE仍可应用且性能损失微小，结果接近最优人工特征。FMAE为动态系统多任务管理提供了实用、数据高效的灵活路径。
原文摘要 · Abstract (English)
Industrial-scale battery management involves various types of tasks, such as estimation, prediction, and system-level diagnostics. Each task employs distinct data across temporal scales, sensor resolutions, and data channels. Building task-specific methods requires a great deal of data and engineering effort, which limits the scalability of intelligent battery management. Here we present the Flexible Masked Autoencoder (FMAE), a flexible pretraining framework that can learn with missing battery data channels and capture inter-correlations across data snippets. FMAE learns unified battery representations from heterogeneous data and can be adopted by different tasks with minimal data and engineering efforts. Experimentally, FMAE consistently outperforms all task-specific methods across five battery management tasks with eleven battery datasets. On remaining life prediction tasks, FMAE uses 50 times less inference data while maintaining state-of-the-art results. Moreover, when real-world data lack certain information, such as system voltage, FMAE can still be applied with marginal performance impact, achieving comparable results with the best hand-crafted features. FMAE demonstrates a practical route to a flexible, data-efficient model that simplifies real-world multi-task management of dynamical systems.
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