Online detection and identification of cathode cracking in Lithium-ion battery cells

Abstract

Cyclic charging and discharging of Lithium-ion (Li-ion) battery cells lead to the contraction and expansion of the battery electrodes. These contractions and expansions result in the development of internal stresses within the electrodes, further culminating in the growth of cracks. Typically, the cracks in anodes lead to an increase in the surface area hence facilitating a faster SEI layer growth, and a lot of research to model such faults has already been conducted in this area. However, when it comes to cracks in the cathode, the research is still a little under-explored. Not detecting the potential cathode crack growth may lead to quick degradation of battery cells which results in capacity fade or resistance growth. If these kinds of faulty batteries are not detected, it may result in hazardous scenarios like battery fires even during nominal usage. Therefore, the real-time monitoring of these cathode cracks is essential for health-conscious battery operation. This paper is an attempt to design such real-time monitoring algorithm that can detect and identify crack growths in the cathode. The algorithm is developed by fusing a variation of the Single Particle Model (SPM) capturing Lithium concentration dynamics in the cathode and an empirical model capturing crack growth — in conjunction with real-time feedback-based coupled filters. The coupled filter consists of two filters working in cascade where the first filter generates a primary residual based on cathode SPM and terminal voltage feedback. Subsequently, the second filter utilizes this primary residual as feedback in conjunction with the empirical crack growth model — ultimately producing an estimate of the crack growth. This estimated crack growth is used to detect and identify the cathode cracking mechanism. Proposed approach is tested with experimental as well as simulation studies, illustrating its effectiveness.