SambaMixer：使用Mamba狀態空間模型預測鋰離子電池的健康狀態

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

鋰離子電池的健康狀態（SOH）是決定電池剩餘容量和剩餘壽命的關鍵參數。本文提出了一種名為SambaMixer的新型結構化狀態空間模型（SSM），用於預測鋰離子電池的健康狀態。所提出的SSM基於MambaMixer架構，旨在處理多變量時間信號。我們在NASA電池放電數據集上評估我們的模型，並展示我們的模型在該數據集上優於最先進的方法。我們進一步引入了一種新型基於錨點的重採樣方法，確保時間信號具有預期長度，同時也作為增強技術。最後，我們使用位置編碼來條件預測樣本時間和循環時間差，以提高我們模型的性能並學習恢復效應。我們的結果證明，我們的模型能夠高精度和穩健地預測鋰離子電池的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.