StateFormer：用于学习历史相关电池状态动态和长期健康预测的多变量变压器
StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting
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中文总结 AI 辅助
本文提出多变量变压器StateFormer，用于预测大规模电池系统退化动态。它能跨时间尺度学习，在统一框架中表征多种电池过程，在合成与真实数据集上预测准确，性能不受噪声和温度影响，弥合实验室与现场差距，为电池相关决策提供智能。
中文摘要 AI 辅助
本文介绍了一种新颖的多变量变压器StateFormer，用于预测大规模电池系统的退化动态。该模型能跨时间尺度学习，从短期热波动到长期老化轨迹，可准确预测包括荷电状态（SOC）、健康状态（SOH）或电池温度等电池状态。通过捕捉长程依赖并识别驱动未来退化的运行条件，它在统一框架中同时表征快速电化学和热过程以及缓慢老化机制。StateFormer在合成和真实世界数据集上都能实现对电池状态估计的稳健且准确的预测。在合成电池组中，当附加电流/电压噪声水平在1%到10%之间且环境温度范围广泛时，以及在从住宅公用事业电池系统收集的五年现场数据中，它都能保持高预测性能。所得模型弥合了实验室到现场的差距，并为维护计划、运行优化和经济决策提供预测智能。
英文摘要
This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of battery states including state of charge (SOC), state of health (SOH), or battery temperature. By capturing long-range dependencies and identifying the operating conditions that drive future degradation, the model simultaneously represents fast electrochemical and thermal processes and slow aging mechanisms within a unified framework. \emph{StateFormer} achieves robust and accurate predictions of battery state estimation across both synthetic and real-world datasets. It maintains high predictive performance under additive current/voltage noise levels ranging from $1\%$ to $10\%$ and a wide range of ambient temperatures in a synthetic battery fleet accommodating different types of electrode chemistry and manufacturing, as well as five years of field data collected from residential utility battery systems. The resulting model bridges the laboratory-to-field gap and provides predictive intelligence for maintenance planning, operational optimization, and economic decision-making.
