Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study
Loading...
Date
2025-06-01
Authors
Sedlařík, Marek
Vyroubal, Petr
Capková, Dominika
Omerdic, Edin
Rae, Mitchell
Mačák, Martin
Šedina, Martin
Kazda, Tomáš
0000-0003-4160-8031Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Altmetrics
Abstract
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.
Description
Citation
ELECTROCHIMICA ACTA. 2025, vol. 524, issue 6, p. 1-22.
https://www.sciencedirect.com/science/article/pii/S0013468625003512
https://www.sciencedirect.com/science/article/pii/S0013468625003512
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en