Capacity fade estimation in electric vehicles Li-ion batteries using artificial neural networks

Ala A. Hussein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

Battery performance degrades as the battery ages. For example, the battery capacity fades away after repeatedly cycling the battery. The degradation rate itself depends on many factors such as the depth-of-discharge (DOD), (dis)charge power, temperature, etc. In this paper, the application of artificial neural network (ANN) in estimating lithium-ion (Li-ion) battery capacity fade in electric vehicles (EVs) is investigated. The focus in this paper is on evaluating the performance of ANN-based techniques in estimating the battery capacity fade in order to: 1) reliably estimate the battery state-of-charge (SOC) using the standard coulomb counting method through the battery life, and 2) accurately predict the battery remaining life. Model derivation and experimental verification are presented in this paper.

Original languageEnglish
Title of host publication2013 IEEE Energy Conversion Congress and Exposition, ECCE 2013
Pages677-681
Number of pages5
DOIs
Publication statusPublished - Dec 31 2013
Event5th Annual IEEE Energy Conversion Congress and Exhibition, ECCE 2013 - Denver, CO, United States
Duration: Sep 15 2013Sep 19 2013

Publication series

Name2013 IEEE Energy Conversion Congress and Exposition, ECCE 2013

Other

Other5th Annual IEEE Energy Conversion Congress and Exhibition, ECCE 2013
Country/TerritoryUnited States
CityDenver, CO
Period9/15/139/19/13

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

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