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A Study on the Continuous and Discrete Wavelet Transform-Based Lithium-Ion Battery Fire Prediction Sensor Technology.

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This study introduces a new sensor system to detect early signs of fire risk in lithium-ion batteries (LIBs). By analyzing electrical signals, it predicts potential failures before they become critical, enhancing battery safety.

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electromagnetic antenna sensorhigh-frequency current transformerlithium-ion batterymicro internal short circuitsthermal runawaywavelet transform

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Chemical Engineering

Background:

  • Conventional lithium-ion battery (LIB) safety systems activate late, after degradation or failure.
  • Early fire risk detection in LIBs is a critical challenge for safety and reliability.

Purpose of the Study:

  • To propose a novel sensor-based diagnostic framework for proactive fire prediction in LIBs.
  • To enable early detection of internal degradation phenomena before macroscopic failure.

Main Methods:

  • Simultaneously monitored low-frequency and high-frequency electrical signatures during battery operation.
  • Employed electromagnetic (EM) antenna and high-frequency current transformer (HFCT) sensors.
  • Utilized continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for time-frequency analysis.

Main Results:

  • Degradation events produced distinct, non-stationary voltage and current signatures localized in specific frequency bands.
  • Signal characteristics intensified with higher C-rates, temperatures, and aging.
  • The approach demonstrated scalability and effectiveness at both cell and module levels.

Conclusions:

  • The combined EM-HFCT sensing and wavelet analysis can differentiate normal operation from incipient faults.
  • This method enables microsecond-scale detection of degradation precursors, outperforming conventional methods.
  • The framework holds potential for advanced battery management systems to prevent fires in energy storage and EVs.