Combining a Data Driven and Mechanistic Model to Predict Capacity and Potential Curve-Degradation

This work compares a state of the art data-driven model to predict the state of health (SoH) in lithium ion batteries with a new prediction model based on the mechanistic framework. The mechanistic approach attributes the degradation to individual components such as loss of available capacity on each electrode as well as loss of cyclable lithium. By combining the mechanistic framework with data-driven models for the component losses based on a design of experiment, we achieve a cycle aging model that can predict capacity degradation as well as degradation-induced changes to the discharge potential curve. Using this cycle aging model alongside with a semi-empirical calendar aging model, we present a holistic aging model that we validate on independent validation tests containing time-variant load profiles. While the purely data-driven model is better at predicting the SoH, the mechanistic model clearly has it advantages in a deeper understanding that can potentially enhance the current methods of tracking and updating the characteristic open-circuit voltage curve over lifetime.