SambaMixer：使用Mamba状态空间模型预测锂离子电池的健康状态

SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models

October 31, 2024

作者: José Ignacio Olalde-Verano, Sascha Kirch, Clara Pérez-Molina, Sergio Martin

cs.AI

摘要

锂离子电池的健康状态（SOH）是决定电池剩余容量和剩余寿命的关键参数。本文提出了一种新颖的结构化状态空间模型（SSM）SambaMixer，用于预测锂离子电池的健康状态。所提出的SSM基于MambaMixer架构，旨在处理多变量时间信号。我们在NASA电池放电数据集上评估了我们的模型，并展示了我们的模型在该数据集上优于现有技术。我们进一步引入了一种新颖的基于锚点的重采样方法，确保时间信号具有预期长度，同时也作为增广技术。最后，我们通过使用位置编码将预测条件设置为样本时间和循环时间差，以提高我们模型的性能并学习恢复效应。我们的结果证明，我们的模型能够高精度和鲁棒地预测锂离子电池的SOH。

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.