Estimation of SOH degradation of coin cells subjected to accelerated life cycling with randomized cycling depths and C-rates
Investigation of li-ion battery state of health (SOH) degradation and its modeling facilitates determination of device warranty and can provide information about the device battery health. For such studies, batteries undergo life-cycling test with fixed cycling depths and charging currents (C-rates) across cycles, and the gathered degradation data is used for model development. However, in the real world, the cycling depth is hardly constant per cycle and varies across users; the SOH estimation of such use-cases is challenging for lab-developed models. In this study, a semi-empirical SOH estimation regression model has been trained using fixed cycling depth and c-rate data and is validated using tests with randomized cycling depth and c-rate variation per cycle. Different upper and lower state of charge (SOC) limits were chosen to stimulate different user profiles. Finally, multiple iterations of this model with different predictor variables have been tested to minimize the estimation error.