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A Hybrid Machine Learning Model for Dynamic Level Detection of Lead-Acid Battery Electrolyte Using a Flat-Plate

Shuai Huang1, Weikang Zhang2, Weiwei Zhang3

  • 1School of Energy and Environment, Southeast University, Nanjing 210096, China.

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PubMed
Summary

This study introduces a hybrid deep learning model, Poly-LSTM, to accurately measure electrolyte levels in lead-acid batteries. It overcomes errors from residual liquid films, improving non-destructive testing accuracy.

Keywords:
capacitive method liquid level measurementhybrid deep learning modellong short-term memorynondestructive evaluationpolynomial feature generationresidual liquid film adhesion layer effect

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Area of Science:

  • Battery Technology
  • Non-Destructive Testing
  • Machine Learning

Background:

  • Lead-acid battery failures can stem from abnormal electrolyte levels.
  • Capacitive methods offer non-invasive electrolyte level detection but suffer from dynamic errors due to residual liquid films on tube walls during rapid level drops.

Purpose of the Study:

  • To develop a novel method for accurate electrolyte level measurement in lead-acid batteries.
  • To mitigate dynamic measurement errors caused by residual liquid film adhesion in capacitive sensing.

Main Methods:

  • A hybrid deep learning model, Poly-LSTM, was developed, combining polynomial feature generation with a Long Short-Term Memory (LSTM) network.
  • Polynomial features were generated to capture nonlinear sensor input effects.
  • LSTM processed these features to model temporal dependencies for accurate liquid level prediction.

Main Results:

  • The Poly-LSTM model demonstrated superior liquid level estimation accuracy compared to other models.
  • At a rapid drop rate of 0.12 mm/s, the model achieved an average absolute error (MAE) of 0.5319 mm.
  • Root mean square error (RMSE) was 0.7180 mm, and mean absolute percentage error (MAPE) was 0.1320%.

Conclusions:

  • The proposed Poly-LSTM model effectively eliminates dynamic measurement errors in capacitive liquid level detection.
  • This hybrid deep learning approach enhances the reliability of non-destructive testing for lead-acid battery electrolyte levels.