Jinwoo Lee1, Daeil Kwon2, and Michael Pecht1,
1Center of Advanced Life Cycle Engineering University of Maryland College Park, USA
2Department of Mechanical Engineering, KonkukUniversity, Seoul, South Korea, 05029
Lithium-ion batteries have been used in a wide variety of applications, ranging from portable electronics to electric vehicles. During repetitive charging and discharging, a battery's capacity fades due to electrochemical reactions such as solid electrolyte interphase (SEI) growth. Lithium-ion batteries reach an end-of-life (EOL) point, after which use is not recommended. However, some unhealthy batteries reach their EOL sooner than expected. A qualification test is usually conducted to evaluate the reliability of Li-ion batteries and classify unhealthy batteries, but this test requires several months. This study developed a data-driven method to reduce the qualification time by detecting anomalies before EOL. The method detects an anomaly in the capacity fade curve of unhealthy batteries based on their capacity fade trend. Since the developed method detects anomalies of unhealthy batteries before EOL, the method is effective for reducing the time for the qualification test of Li-ion batteries.