English(EN) PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics

PhyMamba框架通过物理调制Mamba增强电池健康预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了PhyMamba，一个新颖的两阶段框架，它将基于物理的模型与Mamba序列建模相结合，以改进电池健康预测。该方法通过利用电化学老化原理调制Mamba的序列处理，从而增强了长时健康预测能力，而无需显式识别内部老化参数。在公开数据集上的实验表明，PhyMamba显著优于现有基线，平均误差降低了31.8%，并提供了有利于实际部署的准确性-效率权衡。 AI

影响这项研究可能带来更可靠的电池管理系统，从而提高电动汽车和其他电池供电设备的寿命和性能。

排序理由该集群包含一篇详细介绍特定应用新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Sara Sameer, Yunyi Zhao, Wei Zhang, Minggang Zeng, Wenqing Li, Man-Fai Ng, Yonggang Wen · 2026-08-31 04:00

PhyMamba：物理调制的Mamba用于鲁棒电池健康预测

arXiv:2608.27978v1 Announce Type: new Abstract: 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 p…

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PhyMamba：物理调制的Mamba用于鲁棒电池健康预测

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