Neural Temperature Estimator in Lithium-Ion Batteries

Authors

  • Callebe E. da Cruz Department of Electrical Engineering, Senai University Center, UniSenai Londrina/PR, Brazil.
  • Jorge A. Mondadori Senai Institute of Information and Communication Technology, IST-TIC, Londrina/PR, Brazil.
  • Luis G. F. Espontão Senai Institute of Information and Communication Technology, IST-TIC, Londrina/PR, Brazil.
  • Wesley C. da Silva Department of Electrical Engineering, Senai University Center, UniSenai Londrina/PR, Brazil.
  • Renato K. Miyamoto Department of Electrical Engineering, Senai University Center, UniSenai Londrina/PR, Brazil.
  • Paulo B. Junior Senai Institute of Information and Communication Technology, IST-TIC, Londrina/PR, Brazil.

Keywords:

Lithium Ion Batteries, Artificial Neural Networks, Temperature Prediction

Abstract

The advancement of electrification in the transportation sector has driven the utilization of energy storage systems with Lithium-Ion batteries. In these systems, various factors can compromise the proper operation of the batteries, leading to temperature increases and consequently reducing their useful life. The purpose of this work is to present a methodology for estimating the temperature in Lithium-Ion batteries using artificial neural networks. For this, an experimental structure was set up for temperature acquisition using low-cost sensors. The collected data is used in a neural estimator, which showed an accuracy rate between 96.10% and 99.97%. Experimental results are presented to validate the proposal.

Downloads

Published

2024-10-18

Issue

Vol. 4 No. 1 (2024): CBA2024

Section

Articles