Battery cell state-of-charge estimation using linear parameter varying system techniques

This paper describes a model based method for real time battery cell state-of-charge (SoC) estimation using linear parameter varying (LPV) system techniques. For this class of methods, the applicable structure is one in which the input to output dynamics of the battery can be described by a discrete parameter varying state variable model that includes the SoC as a state. Within this context, the problem of state-of-charge estimation is viewed as a state estimation problem, so that a state estimator is designed using the model. An LPV system technique, combined with input to state stability criteria, is used to analyze the stability and performance of the estimator. Compared with algorithms available in the current literature, such as those employing an extended Kalman filter and sliding mode observers, this method offers good performance with a guarantee of stability, and possesses user friendly tuning with low computational complexity for easy on-board implementation. Experimental results are given which validate the proposed methodology. (C) 2011 Elsevier ay. All rights reserved.