When it comes to battery modeling, there's no magic bullet. The right model for a specific case depends on what questions need to be answered, and which phenomena drive the results of interest.
For example, perhaps we would like a model that can predict the full cycle life of a cell with data from only a small number of cycles. We could build a model that projects coulombic efficiency (CE) and cumulative lithium loss to predict capacity for the Nth cycle. But this model would be useless for a battery that has high CE until the SEI (solid electrolyte interphase) layer catastrophically fails after 200 cycles.
Is the coulombic efficiency model useless, then? If it can't predict SEI failure, then what's the point? If a cell starts out with poor CE, or if the CE trends in a way that we know there will be too much lithium loss, then our CE model can still free up test channels by ruling out some cells earlier.
Some examples of relevant battery modeling approaches are:
Data-based analytical models
This family of models can be used to extract knowledge from experiments and understand the performance and interactions of materials that the company has tested. These models can help scientists determine which cell designs to try next or predict the best cell designs for specific applications.
Finite Element Models and Finite Difference Methods
These types of physics-based models can help scientists understand how cells function and are most useful for understanding single-cell-scale and pack-scale phenomena.
Density Functional Theory (DFT) Model
DFT can be useful to understand specific quantum chemical phenomena such as favored reaction kinetics, how that varies chemical potential, and the impact of introducing different molecules into the system.
Multiscale and Composite Models
Multiscale models incorporate two or more models into a single system. This can model how phenomena at different physical or temporal scales interact to predict outcomes.
Summary
The Micantis team has a wealth of battery modeling expertise in a range of applications including materials science and electrochemistry. The Micantis team can apply our repertoire of proven models and techniques to provide the tools you need to build better batteries faster.