Mathematical modeling of li-ion battery using genetic algorithm approach for V2G applications

Kannan Thirugnanam, T. P. Ezhil Reena Joy, Mukesh Singh, Praveen Kumar

Research output: Contribution to journalArticlepeer-review

118 Scopus citations

Abstract

This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers' data. The battery profiles of different manufacturers' like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers' catalogue) characteristics.

Original languageEnglish
Article number6716979
Pages (from-to)332-343
Number of pages12
JournalIEEE Transactions on Energy Conversion
Volume29
Issue number2
DOIs
StatePublished - Jun 2014
Externally publishedYes

Keywords

  • Batteries
  • capacity loss
  • electric vehicles (EVs)
  • genetic algorithm (GA)
  • vehicle-to-grid (V2G)

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