Estimation strategies for the condition monitoring of a battery system in a hybrid electric vehicle
type: Item
instance of: scholarly article
title: Estimation strategies for the condition monitoring of a battery system in a hybrid electric vehicle
zbMATH Open document ID: 1218.94021
DOI: 10.5402/2011/120351
author: Yong-Cai Geng
author: Sumit K. Garg
published in: ISRN Signal Processing
publication date: 2011-06-14T00:00:00Z
review text: Summary: This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 5907342
describes a project that uses: ADVISOR
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.5402/2011/120351
OpenAlex ID: W2092200183
Wikidata QID: Q58688693
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description: scientific article; zbMATH DE number 5907342