Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks

Ala A. Hussein

Research output: Contribution to journalArticlepeer-review

144 Citations (Scopus)


In this paper, an artificial neural network (ANN) based approach is proposed to estimate the capacity fade in lithium-ion (Li-ion) batteries for electric vehicles (EVs). Besides its robustness, stability, and high accuracy, the proposed technique can significantly improve the state-of-charge (SOC) estimation accuracy over the lifespan of the battery, which leads to more reliable battery operation and prolonged lifetime. In addition, the proposed technique allows accurate prediction of the battery remaining service time. Two identical 3.6-V/16.5-Ah Li-ion battery cells were repeatedly cycled with constant current and dynamic stress test current profiles at room temperature, and their discharge capacities were recorded. The proposed technique shows that very accurate SOC estimation results can be obtained provided enough training data are used to train the ANN models. Model derivation and experimental verification are presented in this paper.

Original languageEnglish
Article number6937156
Pages (from-to)2321-2330
Number of pages10
JournalIEEE Transactions on Industry Applications
Issue number3
Publication statusPublished - May 1 2015


  • Artificial neural networks (ANN)
  • discharge capacity
  • electric vehicle (EV)
  • lithium-ion (Li-ion)
  • state-of-charge (SOC)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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