Detail publikace

Comparative analysis of SVR and ANFIS methods for estimating SOH in lithium ion batteries

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

Originální název

Comparative analysis of SVR and ANFIS methods for estimating SOH in lithium ion batteries

Typ

abstrakt

Jazyk

angličtina

Originální abstrakt

This paper compares two machine learning methods, Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), for estimating SVR parameters. The study was performed on two Samsung INR18650-35E batteries, subjected to 500 cycles using the Constant Current Constant Voltage (CCCV) method at ambient temperature. During this process, the batteries were charged and discharged at a constant rate of 0.5 C.

Klíčová slova

Support Vector Regression, Adaptive Neuro-Fuzzy Inference System, Li-ion

Autoři

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

Vydáno

2. 9. 2024

Místo

Padova

Strany počet

1

BibTex

@misc{BUT189543,
  author="Marek {Sedlařík} and Dominika {Capková} and Tomáš {Kazda} and Petr {Vyroubal}",
  title="Comparative analysis of SVR and ANFIS methods for estimating SOH in lithium ion batteries",
  booktitle="International Symposium ob Beyond Li-Ion Batteries 2024",
  year="2024",
  series="1",
  edition="1",
  pages="1",
  address="Padova",
  note="abstract"
}