%0 Journal Article
%T Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle
%A S. A. Gadsden
%A M. Al-Shabi
%A S. R. Habibi
%J ISRN Signal Processing
%D 2011
%R 10.5402/2011/120351
%X 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. 1. Introduction Condition monitoring is an essential process for fault detection and diagnosis. It involves monitoring system states or parameters over an operational period, where abnormal values or significant changes would indicate a fault. Quite often direct measurements of the states are not available due to limitations in design or cost. In these cases, state and parameter estimation techniques can be used for information extraction. Condition monitoring of systems allows proper maintenance to be scheduled, which helps reduce unscheduled downtime of manufacturing equipment, as well as the cost to repair damaged systems [1, 2]. An important area for condition monitoring is energy management for hybrid electric (HEVs) and battery electric vehicles (BEVs). In general, HEVs have two power sources: a gasoline engine and an electric motor. In full hybrid vehicles, the engine and the motor can operate separately or simultaneously. The motor is used mainly during acceleration, startup, reverse mode, and in regenerative braking. A traction battery pack is used to provide power to the motor. It is recharged by a generator or during regenerative braking. The performance of an HEV is largely dependent on a balance between the gasoline engine and the electric motor, optimized with respect to fuel consumption based on vehicle conditions [3]. Many different types of control methods have been applied to balance the power and energy requirements of HEVs, including fuzzy logic [4¨C6], genetic algorithms [7], dynamic programming [8, 9], Pareto optimization [10], and intelligent mechanism designs [11]. These control strategies rely heavily on the availability of
%U http://www.hindawi.com/journals/isrn.signal.processing/2011/120351/