SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models
This addresses battery health monitoring for applications like electric vehicles and energy storage, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of predicting state of health (SOH) for Li-ion batteries, proposing SambaMixer, a structured state space model based on MambaMixer architecture, which outperforms state-of-the-art methods on the NASA battery discharge dataset.
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.