mlsanityData to 7 Oct 2026

SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models

arXiv 2411.00233 ↗First posted 31 Oct 2024cs.LGcited by 0

On the map: State-space sequence models (S4, Mamba) → Time series forecasting with SSMs

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 26 of its 78 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

26 of 78 references.

References · 26

  1. 2016Densely Connected Convolutional Networkscited by 6,691
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  4. 2017Mixed Precision Trainingcited by 1,135
  5. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  6. 2022Simplified State Space Layers for Sequence Modelingcited by 776
  7. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 607
  8. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 600
  9. 2022Diagonal State Spaces are as Effective as Structured State Spacescited by 350
  10. 2024VideoMamba: State Space Model for Efficient Video Understandingcited by 325
  11. 2016FractalNet: Ultra-Deep Neural Networks without Residualscited by 216
  12. 2022On The Computational Complexity of Self-Attentioncited by 137
14 more
  1. 2022How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projectionscited by 121
  2. 2024Motion Mamba: Efficient and Long Sequence Motion Generationcited by 111
  3. 2024Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understandingcited by 106
  4. 2024The Hidden Attention of Mamba Modelscited by 88
  5. 2018Training Tips for the Transformer Modelcited by 85
  6. 2024Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentationcited by 71
  7. 2022S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spacescited by 41
  8. 2024MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selectioncited by 40
  9. 2024Audio Mamba: Bidirectional State Space Model for Audio Representation Learningcited by 36
  10. 2024MambaStock: Selective state space model for stock predictioncited by 13
  11. 2024Audio Mamba: Pretrained Audio State Space Model For Audio Taggingcited by 9
  12. 2024Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Trainingcited by 3
  13. 2020Robust State of Health Estimation of Lithium-ion Batteries Using Convolutional Neural Network and Random Forestcited by 1
  14. 2023A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cyclescited by 1

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  1. 2026PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics
  2. 2026World Model for Battery Degradation Prediction Under Non-Stationary Aging
  3. 2025Pretrained battery transformer (PBT): A foundation model for battery life prediction
  4. 2025A multi-scale lithium-ion battery capacity prediction using mixture of experts and patch-based MLP
  5. 2025Physics-informed mixture of experts network for interpretable battery degradation trajectory computation amid second-life complexities
  6. 2024DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis
  7. 2026BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking
  8. 2026Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation