Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle. (20th March 2011)

• Record Type: Journal Article
• Authors: Gadsden, S. A.; Al-Shabi, M.; Habibi, S. R.
• Other Names: Cheng L.-M. Academic Editor.; Piazza F. Academic Editor.
• Is Part Of: ISRN signal processing. Volume 2011(2011)
• Publication Date: 2011-03-20
• Subjects: Signal processing -- Periodicals; Signal processing; Periodicals; 621.3822
• DOI: 10.5402/2011/120351
• Languages: English
• ISSNs: 2090-5041

Abstract

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.