An Efficient and Chemistry Independent Analysis to Quantify Resistive and Capacitive Loss Contributions to Battery Degradation

Abstract

Degradation mechanisms leading to deterioration in the battery performance is an inevitable phenomenon. Although there are detailed physics and equivalent circuit based models to predict the losses incurred due to degradation in estimating the health of the battery, they are either incomplete, computationally expensive or both. In this study, we present a very simple and elegant, chemistry independent mathematical analysis, which accurately calculates resistive and capacitive components of cycle-life related losses in a battery system. We demonstrate that discharge profiles obtained at any given degradation state of the battery can be represented by an analytical function, with its origin lying at the heart of battery dynamics, using simple parameter fitting. The model parameters relate to the battery electrochemical potential, resistance and capacity. We first validate our protocol using simulated cycling data from a degrading lithium-ion battery system modeled with detailed electrochemical thermal calculations and show that the estimates of capacity and power fades are >99% accurate using our method. Further, we construct a unique phase space plot of normalized energy, power that gives a compact representation of quantitative and qualitative trend of the degradation state of the system, as well as available power and energy.