Table of Contents
ISRN Signal Processing
Volume 2011, Article ID 120351, 17 pages
Research Article

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada L8S 4L7

Received 4 January 2011; Accepted 6 February 2011

Academic Editors: L.-M. Cheng and F. Piazza

Copyright © 2011 S. A. Gadsden et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.