Electrochemical characterization of LiNi1/3Mn1/3Co1/3O2 at different stages of lifetime

Time: Fri 2020-03-27 10.00

Location: https://kth-se.zoom.us/webinar/register/WN_BfA8UJa9TpyPf7oprzShDA, (English)

Subject area: Chemical Engineering

Doctoral student: Maria Varini , Tillämpad elektrokemi

Opponent: Marca Doeff,

Supervisor: Professor Göran Lindbergh, Kemiteknik, Tillämpad elektrokemi

Abstract

Li-ion batteries have entered our everyday life first as power sources for small electronics, and recently for electric vehicles and stationary storage applications. As the requirements on the performance and lifetime of Li-ion batteries increase and diversify, it becomes paramount to properly understand their electrochemical performance at single-electrode level, and their evolution over cycling. This is crucial for both the design of improved electrode materials, better suited for the most recent applications, but also for accurately predicting the performance decay of existing devices. As the component of focus, the positive electrode was chosen, since it limits both power and energy in Li-ion batteries. Specifically, the material investigated was LiNi1/3Mn1/3Co1/3O2 (NMC111), a state-of-the-art, fully commercial electrode, as well as the precursor for Nirich LiNixMnyCo1 –x –yO2, towards which research is very active. Starting at Beginning of Life, NMC111 was characterized though a combination of electrochemical techniques at varying temperatures (Constant Current cycling, Cyclic Voltammetry, Galvanostatic Intermittent Titration Technique, and Electrochemical Impedance Spectroscopy), which were compared and discussed in terms of electrode response and suitability. Thermodynamic and dynamic properties were obtained, and supported the design of a semi-empirical model for predicting LIBs voltage characteristics. This knowledge was also used to monitor the evolution of NMC111’s performance under high voltage operation, and the possibility of connecting changes in the electrochemical response to specific ageing phenomena: this information could support the creation of physics-based predictive models.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-268936