Identifiability Analysis of a Pseudo-Two-Dimensional Model & Single Particle Model-Aided Parameter Estimation

TL;DR

This paper introduces a methodology for rapid and accurate parameter estimation in battery models, combining model analysis, feature selection, and operating condition considerations to optimize speed and accuracy.

Contribution

It presents a novel parameter identification approach that improves speed and accuracy for P2D battery models by analyzing model identifiability and using low-order models under specific conditions.

Findings

Low-order models estimate parameters 500 times faster at some accuracy loss.

Estimated parameters can initialize P2D models for faster convergence.

Different operating conditions enable tailored model use for efficiency.

Abstract

This contribution presents a parameter identification methodology for the accurate and fast estimation of model parameters in a pseudo-two-dimensional (P2D) battery model. The methodology consists of three key elements. First, the data for identification is inspected and specific features herein that need to be captured are included in the model. Second, the P2D model is analyzed to assess the identifiability of the physical model parameters and propose alternative parameterizations that alleviate possible issues. Finally, diverse operating conditions are considered that excite distinct battery dynamics which allows the use of different low-order battery models accordingly. Results show that, under low current conditions, the use of low-order models achieve parameter estimates at least 500 times faster than using the P2D model at the expense of twice the error. However, if accuracy is a must, these estimated parameters can be used to initialize the P2D model and perform the identification in half of the time.