Lithium-ion batteries (LIBs) are considered the key energy storage technology of the 21st century and have revolutionized the portable electronics and e-mobility segments. However, degradation mechanisms of LIBs, including lithium plating, conductivity, and active material loss, are very challenging to monitor for the Battery Management Systems (BMSs). Even though various non-invasive battery health diagnosis techniques are available, including impedance spectroscopy, pseudo open circuit voltage, differential thermal voltammetry, incremental capacity differential voltage, etc., these methods have difficulty detecting early and sudden battery failures and determining the true state of health (SOH) at a given instant. For this reason, there is still a continued need for other non-invasive, cheap, and reliable monitoring methods that can provide real time SOH and degradation information to the BMSs. In this purpose, we developed a sound vibration-based sensing technique for monitoring the commercial lithium-ion battery’s SOH. The pulse vibrations are directly applied to positive and negative terminals and analyzed by artificial intelligence to identify degradation patterns. Thus, full operando experiments are able to conduct to determine new battery health indicators for the BMSs. This proof of concept study outlines pulse based sound vibrations and is a very effective method to achieve accurate degradation assessments along with early failure indications in LIBs.
Anahtar Kelimeler: Lithium ion, Batteries, Energy, Renewable Energy, Operando Diagnosis