SEMI-EMPIRICAL CYCLING AGING MODELS WITH ENHANCED ACCURACY FOR A NICKEL MANGANESE COBALT CELL

Abstract

Batteries have recently garnered significant attention due to their numerous advantages across various applications, particularly in Electric Vehicles (EVs). However, one of the primary challenges limiting broader industry adoption is the aging of batteries over time. In this study, a semi-empirical aging modelling technique was used to predict battery degradation. Experimental data obtained from a 73 Ah NMC (Nickel Manganese Cobalt) cell to formulate three distinct models based on the Arrhenius Law to predict cyclic aging. Batteries underwent testing at two different temperatures and various depths of discharge (DoD) and C-rates up to approximately 800 cycles. Arrhenius' Law, which relates the rate of a chemical reaction to temperature, provided a solid framework for models. Between the three semi-empirical models developed, SEM-3 demonstrated satisfactory predictive accuracy when compared with the empirical data. Upon examining all data sets, SEM-3 exhibited the lowest Root Mean Square Error (RMSE) value of 0.95 in the model predictions, indicating a high degree of accuracy and reliability in its predictions.