Parameter Estimation for Physics-Based Electrochemical Model Parameterization and Degradation Tracking
Karthik Mayilvahanan, Chemical Engineering, West Group
Abstract: Physics-based electrochemical models are useful tools for optimizing battery cell and material design, managing battery use, and understanding physical phenomena, all of which are key in enabling adoption of batteries to electrify transportation, grid storage, and other high carbon emission industries. Fitting these models to experiments can be a useful approach to determine missing parameters that may be difficult to identify experimentally. In this dissertation, two use cases of this approach — model parameterization and degradation tracking — are explored.
In Chapter 2, an extension of a published model for lithium trivandate cathodes for lithium-ion batteries is outlined. Parameters associated with the thermodynamics and kinetics of Li intercalation and intercalated lithium transport are estimated directly from experimental data. Chapter 3 explores a similar concept of model parameterization, this time focusing on the electrode tortuosity. Beyond model parameterization, parameter estimation can also be useful in the context of tracking degradation by fitting a physics-based model over the course of cycling and interpreting the evolution of the parameter estimates. In all cases, uncertainties in parameter estimates (and their implications) are taken into consideration
Depending on the number of parameters being simultaneously estimated, it can become an onerous task to fit model parameters, especially if the physics-based model cannot easily be enclosed in an efficient optimization algorithm. To this end, machine learning (ML) can be useful. If a ML model is trained offline on synthetic data generated by a battery model to map the observable electrochemical data to parameters in the battery model, the ML model can be deployed to estimate parameters from experiment. These models are referred to as inverse ML models, since they perform the inverse task of a "forward" physics-based model. Chapter 5 introduces this concept in the context of Li-ion battery degradation, and Chapter 6 probes the robustness of the inverse ML approach towards disagreement between experiment and the physics-based model.
Chapter 7 ties together the understanding developed in the previous chapters to estimate parameters for LVO cells cycled at different rates. This study demonstrates how to interpret parameter estimates in conjunction with cycling data to gain mechanistic insight into degradation. A complex map of coupled degradation hypotheses is reduced to a smaller subset of possible mechanisms for exemplary LVO cells.