Publication detail

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

SEDLAŘÍK, M. VYROUBAL, P. CAPKOVÁ, D. OMERDIC, E. RAE, M. MAČÁK, M. ŠEDINA, M. KAZDA, T.

Original Title

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

State-of-HealthLi-ion batteryMachine learningSupport vector regressionGaussian process regressionFeed-forward neural networkAdaptive neuro-fuzzy inference system

Authors

SEDLAŘÍK, M.; VYROUBAL, P.; CAPKOVÁ, D.; OMERDIC, E.; RAE, M.; MAČÁK, M.; ŠEDINA, M.; KAZDA, T.

Released

1. 6. 2025

ISBN

1873-3859

Periodical

ELECTROCHIMICA ACTA

Year of study

524

Number

1 June 2025

State

United Kingdom of Great Britain and Northern Ireland

Pages count

URL

https://doi.org/10.1016/j.electacta.2025.145988

BibTex

@article{BUT198149,
  author="Marek {Sedlařík} and Petr {Vyroubal} and Dominika {Capková} and Edin {Omerdic} and Mitchell {Rae} and Martin {Mačák} and Martin {Šedina} and Tomáš {Kazda}",
  title="Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study",
  journal="ELECTROCHIMICA ACTA",
  year="2025",
  volume="524",
  number="1 June 2025",
  pages="22",
  doi="10.1016/j.electacta.2025.145988",
  issn="1873-3859",
  url="https://doi.org/10.1016/j.electacta.2025.145988"
}