StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Abstract

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.