Model-based health condition monitoring method for multi-cell series-connected battery pack

Rui Xiong*, Fengchun Sun, Hongwen He

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This paper has made efforts to investigate the two key parameters for indicating battery state-of-health. (1) A series dual adaptive extended Kalman filter algorithm has been proposed. (2) An online model-based capacity and resistance estimation scheme has been proposed, which estimates the uncertainty parameters of capacity and resistance to evaluate the health status of battery pack. The result indicates that the prediction inaccuracies of battery are less than 1%. It is helpful for monitoring the health status and safeguarding the safety application of battery system used in electric vehicles.

Original languageEnglish
Title of host publication2016 IEEE Transportation Electrification Conference and Expo, ITEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509004034
DOIs
Publication statusPublished - 22 Jul 2016
Event2016 IEEE Transportation Electrification Conference and Expo, ITEC 2016 - Dearborn, United States
Duration: 27 Jun 201629 Jun 2016

Publication series

Name2016 IEEE Transportation Electrification Conference and Expo, ITEC 2016

Conference

Conference2016 IEEE Transportation Electrification Conference and Expo, ITEC 2016
Country/TerritoryUnited States
CityDearborn
Period27/06/1629/06/16

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