A New Methodology for Early Detection of Failures in Lithium-Ion Batteries

Mario Eduardo Carbonó dela Rosa, Graciela Velasco Herrera (), Rocío Nava, Enrique Quiroga González, Rodolfo Sosa Echeverría, Pablo Sánchez Álvarez, Jaime Gandarilla Ibarra and Víctor Manuel Velasco Herrera
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Mario Eduardo Carbonó dela Rosa: Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 3462580, Mexico
Graciela Velasco Herrera: Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, Mexico City 04510, Mexico
Rocío Nava: Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 3462580, Mexico
Enrique Quiroga González: Instituto de Física, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico
Rodolfo Sosa Echeverría: Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
Pablo Sánchez Álvarez: Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
Jaime Gandarilla Ibarra: Facultad de Ingeniería, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
Víctor Manuel Velasco Herrera: Instituto de Geofísica, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico

Energies, 2023, vol. 16, issue 3, 1-18

Abstract: The early fault detection and reliable operation of lithium-ion batteries are two of the main challenges the technology faces. Here, we report a new methodology for early failure detection in lithium-ion batteries. This new methodology is based on wavelet spectral analysis to detect overcharge failure in batteries that is performed for voltage data obtained in cycling tests, subjected to a standard charge/discharge protocol. The main frequencies of the voltage temporal signal, the harmonic components in the regular cycling test, and a low frequency pattern were identified. For the first time, battery failure can be anticipated by wavelet spectral analysis. These results could be the key to the new early detection of battery failures in order to reduce out-of-control explosions and fire risks.

Keywords: lithium-ion battery; early battery failure detection; wavelet spectral analysis (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: 2023
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