Nonlinear system identification of the Li-ion battery cells for traction applications

Rishi Relan, Jean-Marc Timmermans, Joannes Schoukens

Research output: Chapter in Book/Report/Conference proceedingMeeting abstract (Book)

Abstract

Lithium ion (Li-ion) batteries are attracting significant and growing interest because of their high energy and high power density render them an excellent option for energy storage, particularly in hybrid and electric vehicles as well as an ideal candidate for a wide variety of other applications. In order to develop a complete dynamic model of a lithium ion (Li-ion) battery’s electrical behaviour, which is suitable for virtual-prototyping of battery-powered systems, accurate estimation of the state of charge (SoC) and state of health (SoH) is required. This in-turn depends on the quality of the models which are used for the estimation of these quantities. Hence, even before proceeding towards the modelling step, it is important to fully characterize and understand the electrical behaviour of the battery over its full operating range, so that a flexible and an accurate dynamic model can be developed. In this work, we will fully explore the battery’s nonlinear operating regime to extract useful information based on the frequency domain non-parametric methodology. This advanced methodology allows us to characterize the battery short-term electrical behaviour, in terms of the presence of the nonlinearities in the battery’s voltage response over its full operating range (dependent on the SoC level, Charging/discharging current rates, temperature etc.). This information can later be used by battery modeller to decide on the particular modelling methodology.
Original languageEnglish
Title of host publicationEuropean Research Network on System Identification - ERNSI
Publication statusPublished - 20 Sep 2015
EventERNSI workshop, Varberg, Sweden - Varberg, Sweden
Duration: 20 Sep 201523 Sep 2015

Workshop

WorkshopERNSI workshop, Varberg, Sweden
Country/TerritorySweden
CityVarberg
Period20/09/1523/09/15

Keywords

  • battery
  • nonlinear
  • system identfication

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