Machine learning configurations for state of charge predictions of Li-ion batteries

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201279" target="_blank" >RIV/00216305:26220/26:0201279 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-025-22340-4" target="_blank" >https://www.nature.com/articles/s41598-025-22340-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00706-025-03293-w" target="_blank" >10.1007/s00706-025-03293-w</a>

Result language

angličtina

Original language name

Machine learning configurations for state of charge predictions of Li-ion batteries

Original language description

This work investigates the development of a Nonlinear Autoregressive with Exogenous Input (NARX) Artificial Neural Network (ANN) model to predict the dynamic State-of-Charge (SOC) of a Li-ion battery (LIB) using voltage and Electrochemical Impedance Spectroscopy (EIS) data. To optimize the model's performance, extensive ANN parameter adjustments were investigated and various data structuring techniques were explored. It was found that directly inputting all EIS data corresponding to different SOC levels led to a significant decrease in predictive accuracy. Specifically, the root mean square error (RMSE) for SOC prediction increased by approximately 40% when using frequency-separated EIS data. In contrast, utilizing EIS data from a specific SOC level (0%) significantly improved the model's performance. By selectively excluding input features with lower input-output correlation, the RMSE was reduced by 62%, outlining the significant advantage of using EIS measured at 0% SOC. This result highlights the importance of careful data selection and preprocessing in enhancing the accuracy and efficiency of NN-based SOC estimation. The findings of this study provide valuable insights into the optimal data structuring and feature selection strategies for developing accurate and efficient NN models for battery SOC prediction.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10400 - Chemical sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

MONATSHEFTE FUR CHEMIE

ISSN

0026-9247

e-ISSN

1434-4475

Volume of the periodical

156

Issue of the periodical within the volume

5

Country of publishing house

AT - AUSTRIA

Number of pages

7

Pages from-to

531-537

UT code for WoS article

001435537400001

EID of the result in the Scopus database

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