A self-cognizant dynamic system approach for battery state of health estimation

Guangxing Bai, Pingfeng Wang

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

Abstract

Accurate estimation of the state-of-charge (SoC) and state-of-health (SoH) for an operating battery, as a critical task for battery health management, greatly depends on the validity and generalizability of battery models. Due to the variability and uncertainties involved in battery design, manufacturing, and operation, developing a generally applicable battery physical model is a big challenge. To eliminate the dependency of SoC and SoH estimation on battery physical models, this paper presents a generic data-driven approach for lithium-ion battery health management that integrates an artificial neural network (ANN) with a dual extended Kalman filter (DEKF) algorithm. The ANN is trained offline to model the battery terminal voltages to be used by the DEKF. With the trained ANN, the DEKF algorithm is then employed online for SoC and SoH estimation, where voltage outputs from the trained ANN model are used in DEKF state-space equations to replace the battery physical model. Experimental results are used to demonstrate the effectiveness of the developed model-free approach for battery health management.

Original languageEnglish (US)
Title of host publication2014 International Conference on Prognostics and Health Management, PHM 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959426
DOIs
StatePublished - Feb 9 2015
Externally publishedYes
Event2014 International Conference on Prognostics and Health Management, PHM 2014 - Cheney, United States
Duration: Jun 22 2014Jun 25 2014

Publication series

Name2014 International Conference on Prognostics and Health Management, PHM 2014

Other

Other2014 International Conference on Prognostics and Health Management, PHM 2014
CountryUnited States
CityCheney
Period6/22/146/25/14

Keywords

  • DEKF
  • Li-ion battery
  • Neural networks
  • State-of-health
  • State-ofcharge
  • battery management system

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Software
  • Health Information Management

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  • Cite this

    Bai, G., & Wang, P. (2015). A self-cognizant dynamic system approach for battery state of health estimation. In 2014 International Conference on Prognostics and Health Management, PHM 2014 [7036390] (2014 International Conference on Prognostics and Health Management, PHM 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPHM.2014.7036390