Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk

Jong Won Shon, Donmook Choi, Hyunjae Lee and Sung-Yong Son ()
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Jong Won Shon: Department of Equipment and Fire Protection Engineering, Gachon University, Seongnam-Si 13120, Republic of Korea
Donmook Choi: Department of Equipment and Fire Protection Engineering, Gachon University, Seongnam-Si 13120, Republic of Korea
Hyunjae Lee: Department of Electrical Engineering, Gachon University, Seongnam-Si 13120, Republic of Korea
Sung-Yong Son: Department of Electrical Engineering, Gachon University, Seongnam-Si 13120, Republic of Korea

Energies, 2024, vol. 17, issue 11, 1-15

Abstract: This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires.

Keywords: thermal runaway; lithium-ion battery; expert inference; fuzzy logic; centroid technique; off-gas (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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