Fast and direct diagnosis of states of health for the spent batteries

Fast diagnosis of state of health (SoH) is essential for a second life of the spent batteries due to drastically increasing demands for lithium-ion batteries. However, additional voltage or state of charge (SoC) settings were needed to predict SoH. Here, we suggest a SoH prediction process without any additional settings within 110 seconds (maximum 290 seconds) using machine learning, which consists of 2 steps: protocol classification and SoH regression. Data were generated from 18650-sized cells. There are 3 protocols depending on SoC due to cut-off voltage, and open-circuit voltage and 1.0 C-rate discharge voltage for 10 seconds were collected at every 5% SoC in 188 SoH. The protocol classification model showed 0.9985 of accuracy. Then, protocol data, with current pulses and rest times, corresponding to SoC were collected at every 5% SoC in 1043 SoH, and 12 features were selected from 60 features. The SoH regression model showed 0.850% mean absolute error (MAE), and performance in cases of untrained SoC and misclassification was also superior because the model was trained on all SoC data. In addition, the suggested SoH prediction process can apply to untrained cells, such as different form factors. The performances of 21700-sized cells were improved to 2.600% MAE through feature engineering to the resistance-related features using the ratio between cell capacity and external volume. This paper highlights a fast and direct SoH prediction process without any settings, and feature engineering of untrained cell data to save time and energy for diagnosis of spent batteries.