SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba
  State Space Models
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.
6728687c5fdb8c459e466b5e
José Ignacio Olalde-Verano
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2024-11-04T09:35:47.374Z
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Olalde
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jolalde5
6728687c5fdb8c459e466b5f
Sascha Kirch
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2024-11-04T09:35:50.773Z
63621d1afcea7c03bc3e1f2d
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Sascha Kirch
user
sascha-kirch
6728687c5fdb8c459e466b60
Clara Pérez-Molina
6728687c5fdb8c459e466b61
Sergio Martin
SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba
  State Space Models
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.
A novel structured state space model based on MambaMixer, along with an anchor-based resampling method and positional encodings, accurately and robustly predicts the state of health of Li-ion batteries.
structured state space model SSM MambaMixer multi-variate time signals NASA battery discharge dataset anchor-based resampling method positional encodings recuperation effects