Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198149" target="_blank" >RIV/00216305:26220/26:0198149 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0013468625003512" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0013468625003512</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.electacta.2025.145988" target="_blank" >10.1016/j.electacta.2025.145988</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study

Popis výsledku v původním jazyce

The accurate modeling and prediction of the State-of-Health (SOH) of lithium-ion (Li-ion) batteries are crucial for extending their lifespan, ensuring reliability, and minimizing the costs associated with extensive laboratory testing. This paper investigates the SOH estimation of Li-ion batteries utilizing advanced machine learning (ML) techniques. Specifically, 600 cycles were performed on Samsung INR18650–35E cells using the Constant Current Constant Voltage (CCCV) protocol. The input data for the ML methods were extracted from both charging and discharging cycles to achieve the best possible results. Data-driven models with different methodological foundations were used to predict SOH: Gaussian Process Regression (GPR), Support Vector Regression (SVR), and from the field of Artificial Neural Networks (ANN), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), which utilizes fuzzy logic. The input features for the ML methods were analyzed using Pearson Correlation Analysis (PCA), and additional inputs for the ANFIS method were selected using Exhaustive Search (ES) to identify the optimal combination of inputs with the lowest Root Mean Square Error (RMSE). The individual ML methods were evaluated on datasets of various sizes using the features with the highest correlation to SOH and the full set of features to detect overfitting. Further experiments explored the dependency of RMSE on the amount of training data, and SOH estimation of one battery was performed using training data from another. Overall, experiments show that nearly all methods achieved RMSE below 0.5% for SOH estimation, with SVR proving the most stable technique and ANFIS excelling with meticulously optimized configurations.

Název v anglickém jazyce

Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study

Popis výsledku anglicky

The accurate modeling and prediction of the State-of-Health (SOH) of lithium-ion (Li-ion) batteries are crucial for extending their lifespan, ensuring reliability, and minimizing the costs associated with extensive laboratory testing. This paper investigates the SOH estimation of Li-ion batteries utilizing advanced machine learning (ML) techniques. Specifically, 600 cycles were performed on Samsung INR18650–35E cells using the Constant Current Constant Voltage (CCCV) protocol. The input data for the ML methods were extracted from both charging and discharging cycles to achieve the best possible results. Data-driven models with different methodological foundations were used to predict SOH: Gaussian Process Regression (GPR), Support Vector Regression (SVR), and from the field of Artificial Neural Networks (ANN), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), which utilizes fuzzy logic. The input features for the ML methods were analyzed using Pearson Correlation Analysis (PCA), and additional inputs for the ANFIS method were selected using Exhaustive Search (ES) to identify the optimal combination of inputs with the lowest Root Mean Square Error (RMSE). The individual ML methods were evaluated on datasets of various sizes using the features with the highest correlation to SOH and the full set of features to detect overfitting. Further experiments explored the dependency of RMSE on the amount of training data, and SOH estimation of one battery was performed using training data from another. Overall, experiments show that nearly all methods achieved RMSE below 0.5% for SOH estimation, with SVR proving the most stable technique and ANFIS excelling with meticulously optimized configurations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EH22_008%2F0004617" target="_blank" >EH22_008/0004617: Konverze a skladování energie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

ELECTROCHIMICA ACTA

ISSN

0013-4686

e-ISSN

1873-3859

Svazek periodika

524

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

22

Strana od-do

1-22

Kód UT WoS článku

001449788300001

EID výsledku v databázi Scopus

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