Derivation and Comparison of Open-loop and Closed-loop Neural Network Battery State-of-Charge Estimators

Ala A. Hussein

Research output: Contribution to journalConference articlepeer-review

23 Citations (Scopus)

Abstract

This paper presents two artificial neural network (ANN) based algorithms for battery state-of-charge (SOC) estimation. The SOC is an important quantity that must be estimated in real-time in many applications. ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. The first algorithm presented in this paper has an open-loop structure and known as nonlinear input output (NIO) feed-forward algorithm, while the second is closed loop called nonlinear autoregressive with exogenous input (NARX) feed-back algorithm. A pulse-discharge test is performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.

Original languageEnglish
Pages (from-to)1856-1861
Number of pages6
JournalEnergy Procedia
Volume75
DOIs
Publication statusPublished - 2015
Event7th International Conference on Applied Energy, ICAE 2015 - Abu Dhabi, United Arab Emirates
Duration: Mar 28 2015Mar 31 2015

Keywords

  • Artificial neural network (ANN)
  • battery
  • state-of-charge (SOC)

ASJC Scopus subject areas

  • Energy(all)

Fingerprint

Dive into the research topics of 'Derivation and Comparison of Open-loop and Closed-loop Neural Network Battery State-of-Charge Estimators'. Together they form a unique fingerprint.

Cite this