Machine Learning-Enhanced Electrical Circuit Model Parameterization for Battery Cells: Reducing Experimental Workload Through GITT Testing with Altair RapidMiner® Beond Srl

Format:

Book

Conference/Event

Author/Creator:

Giuliano, Luca, author.

Contributor:

Canella, Nicholas

Nefat, Damir

Peretto, Lorenzo

Conference Name:

SETC2025: 29th Small Powertrains and Energy Systems Technology Conference (2025-11-10 : Florence, Italy)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

This study addresses the challenge of reducing the experimental workload involved in characterizing battery cell behavior as a function of state of charge and temperature. Galvanostatic Intermittent Titration Technique tests were carried out in a climate chamber across a wide temperature range, from -20 °C to 70 °C, with 10 °C intervals. The voltage and current response data collected from these tests were used to train several machine learning algorithms.The trained models could then be used to predict the cell voltage response every 5 °C from -15 °C to 55 °C. While the models were experimentally validated at 15 °C, 25 °C, and 35 °C, the predicted voltages across this range contribute to enhancing the characterization process.In particular, the inclusion of these predicted voltage profilescombined with the experimental data collected every 10 °C from -20 °C to 70 °Callows for the creation of more accurate lookup tables for the parameters of the equivalent circuit model. These parameters include the open circuit voltage, series resistance, and multiple resistor-capacitor pairs representing dynamic electrochemical behavior. This approach results in significantly improved parameter estimation compared to using only the original experimental dataset

Notes:

Vendor supplied data

Publisher Number:

2025-32-0097

Access Restriction:

Restricted for use by site license