Improved Electrochemical-Mechanical Parameters Estimation Technique for Lithium-Ion Batteries Models

Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. The reliability of these models depends on the accurate estimation of internal electrochemical and mechanical parameters, many of which are not directly measurable and must be identified through model-based fitting of experimental data. Unlike other parameter estimation procedures, this paper introduces a novel approach that integrates mechanical measurements with the electrical data, with a specific application to lithium iron phosphate (LFP) cells. An error analysis – based on the Root Mean Square Error (RMSE) and on the confidence ellipses – confirms that the inclusion of mechanical measurements significantly improves the accuracy of the identified parameters and the reliability of the algorithm compared to approaches relying just on electrochemical data. Two scenarios are analyzed: in the first, a teardown of the cell provides direct measurements of electrode thicknesses and number of layers; in the second, these values are treated as additional unknown parameters. In the teardown case, the electro-chemo-mechanical model achieves significantly lower RMSEs and smaller confidence ellipses, confirming its superior reliability. In the second, while the RMSE values are similar to those of the purely electrochemical model, the smaller ellipses still indicate better consistency and convergence in the parameter estimates.