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Improved Impedance Measurements for Electric Vehicles with Reconfigurable Battery Systems

  • Bernhard Liebhart
  • , Simon Diehl
  • , Michael Schmid
  • , Christian Endisch
  • , Ralph Kennel
  • Ingolstadt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Embedding impedance spectroscopy into onboard applications such as electric vehicles increases the diagnostic capabilities of battery management systems largely. A novel approach is presented and experimentally investigated throughout this work, whereby the load current excitation injected by the electric powertrain is pulsed by transistors. With a reconfigurable battery system, this form of active impedance spectroscopy can be used without further hardware. To obtain high-quality impedance data, a broadband data processing algorithm in the frequency domain is employed that deals inherently with both pulsing and non-pulsing data. An experimental setup is constructed to evaluate the proposed methods under real world conditions and the measurement results are in great accordance with laboratory reference measurements. Finally, a linear regression model acts as filter to suppress outliers, smooth the impedance spectra and thus describe the time-varying impedance of each cell.

Original languageEnglish
Title of host publicationProceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1736-1742
Number of pages7
ISBN (Electronic)9781728163444
DOIs
StatePublished - 24 May 2021
Event12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 - Virtual, Singapore, Singapore
Duration: 24 May 202127 May 2021

Publication series

NameProceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

Conference

Conference12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Country/TerritorySingapore
CityVirtual, Singapore
Period24/05/2127/05/21

Keywords

  • Artificial Neural Network
  • Impedance Spectroscopy
  • Power MOSFET
  • Reconfigurable Battery System

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