SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba
  State Space Models
Authors: name: José Ignacio Olalde-Verano, name: Sascha Kirch, name: Clara Pérez-Molina, name: Sergio Martin

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.