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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

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

20201 - Electrical and electronic engineering

Project

<a href="/en/project/EH22_008%2F0004617" target="_blank" >EH22_008/0004617: Energy conversion and storage</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Name of the periodical

ELECTROCHIMICA ACTA

ISSN

0013-4686

e-ISSN

1873-3859

Volume of the periodical

524

Issue of the periodical within the volume

6

Country of publishing house

GB - UNITED KINGDOM

Number of pages

22

Pages from-to

1-22

UT code for WoS article

001449788300001

EID of the result in the Scopus database

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