Mechanistic investigation of silicon-graphite/LiNi0.8Mn0.1Co0.1O2 commercial cells for non-intrusive diagnosis and prognosis

Due to their high energy density, lithium-ion batteries with blended silicon-graphite (Si-Gr) anodes and nickel-rich (NMC) cathodes have been regarded as one of the most promising technologies for next-generation consumer electronics and electric vehicles. However, there are still several technical challenges to overcome for successful wide-spread adoption; in particular, deciphering the degradation phenomena remains complex and challenging, as the blended nature of the electrode creates a new paradigm, with the Si/Gr ratio likely changing with aging. Although ex-situ techniques have been used, a set of in-operando tools that enable diagnosis and prognosis on this technology has yet to be developed. Herein, we present a mechanistic investigation that generates a complete degradation mapping coupled with proposed aging features of interest, to attain accurate diagnosis and prognosis. The mechanistic model allows analyzing aging modes that display incubation periods as a potential prelude to thermodynamic plating, and the identification via incremental capacity of unique silicon features that change predictably as it degrades. A comprehensive look-up table summarizing key features is provided to provide support both to scientists and engineers on designing next-generation battery management systems for this technology.