Static electric equivalent circuit of commercial lithium-ion battery cells using genetic algorithms

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

2 Citations (Scopus)

Abstract

In this work, a static electrical equivalent circuit
(EEC) is proposed based on the charge and discharge
performance of lithium-ion battery cells (LiBs), which can be
obtained either directly from datasheet, or by a simple constant
current charge/discharge cycle. The battery cell behavior is
described by a non-linear mathematical representation with
only dependency on the current rate (C-rate) and the state of
charge (SoC) of the battery cells. Its coefficients are optimized
using a single objective genetic algorithm and the methodology
is tested on five different chemistries and various shaped
commercial LiB cells. The results have shown a good agreement
between the simulated and the experimental values. A simple
and reasonably accurate EEC is proposed in this work, which
can be part of the performance evaluation criterion of electric
drive of the vehicles as well as investigating a potential energy
storage system.
Original languageEnglish
Title of host publicationIEEE Vehicle Power and Propulsion Conference (IEEE-VPPC 2019)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781728112497
DOIs
Publication statusPublished - 9 Jan 2020
Event2019 IEEE Vehicle Power and Propulsion Conference (VPPC), Hanoi, Vietnam. -
Duration: 14 Oct 201817 Oct 2019

Conference

Conference2019 IEEE Vehicle Power and Propulsion Conference (VPPC), Hanoi, Vietnam.
Period14/10/1817/10/19

Bibliographical note

Funding Information:
As a next step, a second or higher order modeling EEC is intended to be investigated, for whether the error can be further minimized at the critical low/high SoC/DoD regions or not, and at what extent/cost. Also, more C-rates and different ambient temperatures can be considered in order to propose a theoretically more robust and reliable simple static EEC. In this regard, the parameters can be evaluated with a multi objective genetic algorithm, to further compensate on the computational time and the modeling efficiency ACKNOWLEDGMENT This research has been made possible, thanks to the research project GHOST, BATTLE and GEIRI. This research is part of the GHOST project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 770019. Further, we acknowledge Flanders Make for the support to our research team.

Funding Information:
This research has been made possible, thanks to the research project GHOST, BATTLE and GEIRI. This research is part of the GHOST project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 770019. Further, we acknowledge Flanders Make for the support to our research team.

Publisher Copyright:
© 2019 IEEE.

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