Reduction of Li-ion Battery Qualification Time Based on Prognostics and Health Management

Lithium-ion (Li-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 growth. Li-ion batteries reach an end-of-life (EOL) point, after which using them 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 paper develops a datadriven method to reduce the qualification time by detecting anomalies before EOL. This 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 in reducing the time for the qualification test of Li-ion batteries.