Summary Lithium ion cells, when cycled, exhibit a two‐stage degradation behavior characterized by a first linear stage and a second nonlinear stage where degradation is rapid. The multitude of degradation phenomena occurring in lithium ion batteries complicates the understanding of this two‐stage degradation behavior. In this work, a simple and intuitive model is presented to analyze the coupled effect of resistance growth and the shape of the state of charge (SOC)‐open circuit voltage (OCV) relationship in representing the complete degradation behavior. The model simulations demonstrate that a single resistance that increases linearly on cycling can capture the transition from slow to fast degradation, primarily due to the shape of the SOC‐OCV curve. Further, the model simulations indicate that the shape of the degradation curve depends strongly on the magnitude of current at the end of discharge of the cycling protocol. To verify these observations, specific experiments are designed with minimal capacity loss but with shrinking operating voltage ranges that result in shrinking operating OCV range. The results of the experiments validate the observations of model simulations. Further, long‐term cycling experiment with a commercial lithium ion cell shows that the operating OCV range shrinks substantially with aging and is a major reason for the observed accelerated degradation. The analysis of the present work provides significant insights towards developing simple semiempirical models suitable for battery life management in microcontrollers.

Kinetics of oxidation of chloride ion is studied on both active platinum electrode and that undergoing transient passivation. Experiments are conducted in concentrated NaCl solution at rotating disk electrode. It is observed that on the active platinum electrode, oxidation is very fast, and hence the current density is controlled by the ohmic resistance of the solution. Electrode kinetics becomes important only when the electrode is passivated to a significant extent. Kinetics of chloride oxidation on the electrode undergoing passivation is modeled using the ButlerÀVolmer equation, in which the contribution from the ohmic resistance of the solution is incorporated. Two regimes of passivation are identified. The first is the fast regime corresponding to the formation of the platinum oxide monolayer. In this regime, the rate of passivation is first order in the concentration of the metal sites on the surface. In the slow passivation regime, the exchange current density for chloride oxidation is found to vary inversely with square root of time. This regime is modeled by considering unsteady diffusion of oxygen ions through the metal lattice. From this analysis it is concluded that the chloride oxidation current is almost totally contributed by a small fraction of the active metal sites which are continuously being regenerated as a result of diffusion of oxygen ions from the surface into the bulk of the metal.

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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.

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