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.
Highlights
Www.nature.com/scientificreports electrolytes, leads primarily to capacity fade[8,9,10]
The computational expense of these detailed models is somehow mitigated through what are called reduced order models (ROM)[15,16] where significant reduction in computational time is achieved by approximating partial differential equations in the electro-chemical-thermal models (ECT) model and condensing the detailed physics through, either ordinary differential equations or linear algebraic expressions, via volume averaging
The cell voltage is determined primarily by the difference in chemical potential between anode and cathode, i. e. the potential at which the electrodes exchange Li+, which in turn gets modified by cell internal resistance which itself is dependent on operational parameters
Summary
Www.nature.com/scientificreports electrolytes, leads primarily to capacity fade[8,9,10]. A drop in the discharge voltage due to resistance, leads to the profile reaching the minimum voltage cut-off faster during discharge, manifesting as a shrinkage on the capacity/time axis As mentioned, this is a recoverable loss due to its reversible nature, in general. Among current approaches, detailed physics based models, incorporating all the relevant electrochemical processes in the cell are capable of predicting battery degradation states accurately. These protocols take into account all the conservation equations, leading to a system of partial differential equations, which are numerically solved on platforms like MATLAB, COMSOL to obtain detailed profiles. These plots, quantitatively and qualitatively predict the type of degradation, the losses incurred and the shifts between resistive and capacity losses over cycling, giving a holistic view of the status of the system
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