PhyMamba：用于鲁棒电池健康预测的物理调制Mamba模型

中文摘要 AI 辅助

电池健康预测是电池管理系统（BMS）的核心功能，但受运行条件依赖性和传感器噪声影响，从BMS信号进行长期健康预测仍具挑战性。本文提出PhyMamba，这是一种两阶段的物理调制Mamba框架，将电化学老化融入序列建模。PhyMamba无需明确识别通常依赖侵入式测量的内部老化参数。在第一阶段，轻量Mamba编码器先处理BMS信号，生成的潜在表示经老化参数化模块转换为物理感知的老化特征。在第二阶段，定制的Mamba预测骨干执行多周期预测，其中物理规律被紧密整合以调节模型的内部时间更新，使其向与退化一致的演化方向发展。在三个公共数据集上针对多个预测 horizon 开展的实验表明，PhyMamba实现了最优的聚合性能，与多种基线方法相比，整体平均误差降低了31.8%。PhyMamba还提供了优化的精度-效率权衡，支持鲁棒电池健康预测的实际部署。

英文摘要

Battery health prognostics is a core function in battery management systems (BMSs), yet long-horizon health forecasting from BMS signals remains challenging due to operating-condition dependency and sensor noise. In this paper, we propose PhyMamba, a two-stage physics-modulated Mamba framework that integrates electrochemical aging into sequence modelling. PhyMamba does not require explicit identification of internal aging parameters, which often relies on intrusive measurements. In stage-1, a lightweight Mamba encoder first processes BMS signals and produces a latent representation that is transformed via an aging parameterization module, into physics-informed aging features. In stage-2, a customized Mamba forecasting backbone performs multi-cycle prediction, where physics is tightly integrated to regulate the model's internal temporal updates toward degradation-consistent evolution. Experiments on three public datasets under multiple forecast horizons show that PhyMamba achieves the best aggregated performance, with an overall mean error reduction of 31.8% compared with a diverse range of baselines. PhyMamba also offers an optimized accuracy-efficiency trade-off, which supports practical deployment for robust battery health prognostics.

发表机构

• Singapore Institute of Technology（新加坡理工学院）
• National University of Singapore（新加坡国立大学）
• Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
• Nanyang Technological University (NTU)（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。