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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10400 - Chemical sciences

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

MONATSHEFTE FUR CHEMIE

ISSN

0026-9247

e-ISSN

1434-4475

Svazek periodika

156

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

7

Strana od-do

531-537

Kód UT WoS článku

001435537400001

EID výsledku v databázi Scopus

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