Exploring Operando Electrochemical Techniques to Track Degradation Mechanisms in Li-Ion Batteries

Abstract

The battery field has recently received unprecedented attention and investments in order to handle the upcoming demands of energy storage notably from the electric mobility sector. The pursuit of higher performance batteries that store more energy, charge faster and have a longer life cycle has been converted into studies for new electrode materials, coatings, electrolytes, among others that often find the common key challenge of the performance decline. The precise identification of the degradation mechanisms and their tracking along aging is thus essential to pave the way for the next generation batteries. For that several methods are employed although most of them are not in operando, not following the practical cell operation. In that context, this paper introduces a set of operando electrochemical techniques that are still largely underexploited but that can be coupled and offer broader comprehension of the degradation mechanisms through deeper than usual analysis of electrochemical data. Taking insertion cells (like Li ion or Na ion batteries) as example it is first shown how differential techniques like incremental capacity analysis (ICA or dQ/dV vs V) and differential voltage analysis (DVA or dV/dQ vs Q) allow to identify main degradation mechanisms in the positive and in the negative electrodes separately by following their active material masses and their curves dynamics (relative displacement of the potential x capacity curves, the slippage) in a two electrodes configuration. For that the in house and free access developed software Electrochemical Visualization Application (EVA) is employed. In addition, using defined protocols, other techniques as Distribution of Relaxation Times (DRT) are coupled to those first analyses in order to complement the understanding of the degradation mechanisms in each electrode. This presentation intends to show an introduction of how to deeper analyze electrochemical techniques to follow loss of mass and lithium ion inventory from each electrode along aging, how to use the DRT with the ICA and DVA to identify the evolution of Impedance from each electrode along aging and how to build protocols that allow to couple those techniques minimizing any eventual degradation interference from those techniques, that all shown through experimental results and with the aid of a free and open software for the part of the ICA and DVA analyses. Figure 1