title: SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba
    State Space Models
summary: 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.
paper_authors._id: 6728687c5fdb8c459e466b5e
paper_authors.name: José Ignacio Olalde-Verano
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T09:35:47.374Z
paper_authors._id: 65eb4e992dcb1707395914db
paper_authors.avatarUrl: /avatars/f4e39076a437efc82a87c038be7d849b.svg
paper_authors.fullname: Olalde
paper_authors.type: user
paper_authors.user: jolalde5
paper_authors._id: 6728687c5fdb8c459e466b5f
paper_authors.name: Sascha Kirch
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T09:35:50.773Z
paper_authors._id: 63621d1afcea7c03bc3e1f2d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63621d1afcea7c03bc3e1f2d/8ANwwN4Ex-18aoFGKtv4T.png
paper_authors.fullname: Sascha Kirch
paper_authors.type: user
paper_authors.user: sascha-kirch
paper_authors._id: 6728687c5fdb8c459e466b60
paper_authors.name: Clara Pérez-Molina
paper_authors._id: 6728687c5fdb8c459e466b61
paper_authors.name: Sergio Martin
paper_title: SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba
    State Space Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: structured state space model SSM MambaMixer multi-variate time signals NASA battery discharge dataset anchor-based resampling method positional encodings recuperation effects