Publication detail

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

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

Original Title

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

Type

abstract

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

2. 9. 2024

Location

Padova

Pages count

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"
}