用Mamba架构预测锂电池健康状态，精度更高更稳定。
SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models
- 基于MambaMixer结构建模多变量时序数据
- 在NASA数据集上优于现有最先进方法
- 引入锚点重采样与位置编码提升性能
锂离子电池的健康状态（SOH）是决定其剩余容量和寿命的关键参数。本文提出SambaMixer，一种新型结构化状态空间模型（SSM），用于预测锂离子电池的健康状态。该模型基于MambaMixer架构，专为处理多变量时间序列信号设计。我们在NASA电池放电数据集上评估了该模型，结果表明其在该数据集上优于现有最先进方法。我们进一步提出一种新颖的基于锚点的重采样方法，确保时间信号长度符合预期，同时作为数据增强手段。最后，通过使用位置编码对采样时间与循环时间差进行条件化，提升模型性能并学习恢复效应。实验结果证明，该模型能够以高精度和强鲁棒性预测锂离子电池的健康状态。
原文摘要 · Abstract (English)
The state of health (SOH) of a Li-ion battery is a critical parameter that determines the remaining capacity and the remaining lifetime of the battery. In this paper, we propose SambaMixer a novel structured state space model (SSM) for predicting the state of health of Li-ion batteries. The proposed SSM is based on the MambaMixer architecture, which is designed to handle multi-variate time signals. We evaluate our model on the NASA battery discharge dataset and show that our model outperforms the state-of-the-art on this dataset. We further introduce a novel anchor-based resampling method which ensures time signals are of the expected length while also serving as augmentation technique. Finally, we condition prediction on the sample time and the cycle time difference using positional encodings to improve the performance of our model and to learn recuperation effects. Our results proof that our model is able to predict the SOH of Li-ion batteries with high accuracy and robustness.
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