Effect of Li-Ion Battery Form Factor on the SoH Degradation under Randomized Charge-Discharge Cycles and C-Rates

The growing need for wearable devices has increased the interest in thin, flexible power sources. A fundamental understanding of the degradation of power sources in consumer electronics devices is required to determine survivability over the useful life and inform the user of the battery's state-of-health health and remaining useful life in real-time. Battery testing in prior work often involves accelerated life cycling with fixed charge-discharge limits and fixed charge and discharge currents (C-rates) per cycle. Fixed conditions testing does not represent the real-world use case scenarios of products with random end-ofcycling limits. Furthermore, the models developed using fixed cycling data are often tested on datasets with the same characteristics as the training data, i.e., fixed depth of cycling and C-rates. The model accuracy in such cases can often be misleading as the model is not tested on randomized cycling/charging datasets, which are the norm in real-world use-case scenarios. To this end, the current work focuses on accelerated life cycling tests of batteries with random variations in charge-discharge depth and C-rates, individually and simultaneously. Multiple iterations of the SOH estimation models have been presented with different predictor variables to minimize the model validation error. Four different SOC-bound limits were chosen for the randomized depth of cycling of the battery samples, and the C-rate was varied between 0.5C-2.5C. In addition, four different model variants were chosen considering the voltage profile of the battery using charging and discharging. All the model variants estimated the random depth of cycling SOH with high accuracy, the most common type of battery use-case scenario occurring in the real world. All the model variants estimated the random C-rate, combined randomized depth of cycling, and randomized C-rate SOH with intermediate accuracy.