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

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.