While relatively young, the mechanistic modeling approach has proven extremely versatile and effective for Lithium-ion battery diagnosis and prognosis and it has gained tremendous traction in the past decade. The approach is relying on digital twins built from different matchings of the individual voltage response of each electrode. The digital twins enable quantification of the degradation modes and open the gate for material-based diagnosis and prognosis without the need for complex models. From previous work in our laboratory, the method allowed to conceptually explain counter intuitive concepts such as hidden degradation mechanisms, overdischarge induced capacity, and the sudden apparition of knees.

This presentation will reflect on a decade worth of discussion and validation of several of the key concepts of digital twins for advanced diagnosis and prognosis since the seminal publications in the early 2010s. It will also introduce the next chapter with several new directions to enhance its application to more complex case figures. In the past decade since proposing our version of the framework, it was used to diagnose the degradation of several hundred cells of multiple chemistries and blends. It made possible to explain and predict the apparition of knees with the concept of hidden mechanisms and to emulate the impact of kinetics. The approach also proved useful to investigate overdischarge, overcharge, and to generate big-data with millions of synthetic voltage curves enabling the development of advanced diagnosis and prognosis tools. Looking forward, the approach can still be improved and some new directions will be detailed here with proof-of-concept simulations. This includes the simulation of blended and inhomogeneous electrodes that are destined to play a big role for the diagnosis and prognosis of next generation cells. Moreover, calculations could be done outside of constant current which could allow a much wider field implementation of better diagnosis and prognosis tools for deployed systems.