Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/29504
Title: | Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle |
Authors: | Gadsden SA Al-Shabi M Habibi SR |
Department: | Mechanical Engineering |
Keywords: | 4007 Control Engineering, Mechatronics and Robotics;40 Engineering;4010 Engineering Practice and Education;7 Affordable and Clean Energy |
Publication Date: | 13-Apr-2011 |
Publisher: | Hindawi |
Abstract: | 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. |
metadata.dc.rights.license: | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND |
URI: | http://hdl.handle.net/11375/29504 |
metadata.dc.identifier.doi: | https://doi.org/10.5402/2011/120351 |
ISSN: | 2090-5041 2090-505X |
Appears in Collections: | Mechanical Engineering Publications |
Files in This Item:
File | Description | Size | Format | |
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001-120351.pdf | Published version | 6.69 MB | Adobe PDF | View/Open |
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