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Gas-Informed Machine Learning Framework for Stage Classification and Early Forecasting of Battery Degradation.

Yiqing Lu1, Jianli Zou2, Liang Zhang1

  • 1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.

ACS Applied Materials & Interfaces
|April 27, 2026
PubMed
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Online electrochemical mass spectrometry (OEMS) gas evolution provides early battery degradation detection and accurate long-term capacity forecasting. This chemically specific method complements electrical signals for improved battery health management.

Area of Science:

  • Electrochemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Traditional battery health prediction relies on electrical signals (capacity, voltage, impedance).
  • These signals are chemically nonspecific and often lag behind actual failure onset.
  • Early identification of battery degradation stages is crucial for reliable performance.

Purpose of the Study:

  • To demonstrate operando gas evolution via OEMS for early battery degradation identification.
  • To develop models for distinguishing degradation stages and forecasting capacity.
  • To establish gas evolution as a chemically grounded complement to electrical monitoring.

Main Methods:

  • Online Electrochemical Mass Spectrometry (OEMS) to measure gas evolution (CO, CO2, C2H4).
Keywords:
degradation forecastingdegradation stage classificationgas evolutionlithium-ion batterymachine learning

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Last Updated: Apr 30, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

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  • Principal Component Analysis (PCA) and K-means clustering for label-free stage classification.
  • A single-shot forecasting model fusing capacity/SOH history with OEMS data.

    • Main Results:

    • The stage classifier achieved a 3.6% misclassification rate, identifying nonlinear transitions 32.0 ± 17.2 cycles before the Bacon-Watts knee.
    • The forecaster achieved 1.7-2.9% RMSE at 100-cycle horizons on held-out cells.
    • Gas evolution signatures enabled earlier degradation detection and more accurate long-horizon capacity forecasting.

    Conclusions:

    • Operando gas evolution provides SOC-resolved chemical signatures for earlier degradation identification.
    • Gas evolution monitoring, combined with electrical data, enhances battery health management.
    • This approach motivates integrating compact off-gas sensing for cycle-level battery management.