Detail publikace

Comparison of machine learning techniques for estimating battery health

SEDLAŘÍK, M. KAZDA, T. CAPKOVÁ, D. VYROUBAL, P.

Originální název

Comparison of machine learning techniques for estimating battery health

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

The increasing number of lithium-ion (Li-ion) batteries across a wide range of industries emphasizes their reliability, which is primarily achieved through accurate estimation of State-of-Health (SOH). Numerous methods exist for determining SOH, and this study focuses on estimating battery life parameters using machine learning (ML), which appears to be a fast and promising approach for this application. Experimental measurements were performed using Constant Voltage Constant Current (CCCV) tests, from which discharge cycle parameters were extracted for SOH estimation. The suitability of these parameters was verified using Pearson correlation analysis, demonstrating their appropriateness for estimation as well as their redundancy, which adversely affects overfitting. Two types of ML methods are compared, each with its own advantages and disadvantages: Support Vector Regression (SVR) and Gaussian Process regression (GPR).

Klíčová slova

Support Vector Regression, Gaussian Process Regression, Li-ion battery

Autoři

SEDLAŘÍK, M.; KAZDA, T.; CAPKOVÁ, D.; VYROUBAL, P.

Vydáno

21. 7. 2024

Nakladatel

Brno University of Technology Faculty of Electrical Engineering and Communication of the Brno University of Technology

Místo

Brno

ISBN

978-80-214-6257-1

Kniha

Advanced Batteries Accumulators and Fuel Cells – 25th ABAF

Edice

25

Strany od

126

Strany do

128

Strany počet

3

URL

https://www.aba-brno.cz/download/2024-ABAF-Proceeding.pdf

BibTex

@inproceedings{BUT189520,
  author="Marek {Sedlařík} and Tomáš {Kazda} and Dominika {Capková} and Petr {Vyroubal}",
  title="Comparison of machine learning techniques for estimating battery health",
  booktitle="Advanced Batteries Accumulators and Fuel Cells – 25th ABAF",
  year="2024",
  series="25",
  pages="126--128",
  publisher="Brno University of Technology Faculty of Electrical Engineering and Communication of the Brno University of Technology",
  address="Brno",
  isbn="978-80-214-6257-1",
  url="https://www.aba-brno.cz/download/2024-ABAF-Proceeding.pdf"
}