Online parameter identification of ultracapacitor models using the extended Kalman filter

Abstract

Ultracapacitors (UCs) are the focus of increasing attention in electric vehicleand renewable energy system applications due to their excellent performance in terms ofpower density, efficiency, and lifespan. Modeling and parameterization of UCs play animportant role in model-based regulation and management for a reliable and safe operation.In this paper, an equivalent circuit model template composed of a bulk capacitor, asecond-order capacitance-resistance network, and a series resistance, is employed torepresent the dynamics of UCs. The extended Kalman Filter is then used to recursivelyestimate the model parameters in the Dynamic Stress Test (DST) on a specially establishedtest rig. The DST loading profile is able to emulate the practical power sinking andsourcing of UCs in electric vehicles. In order to examine the accuracy of the identifiedmodel, a Hybrid Pulse Power Characterization test is carried out. The validation resultdemonstrates that the recursively calibrated model can precisely delineate the dynamicvoltage behavior of UCs under the discrepant loading condition, and the onlineidentification approach is thus capable of extracting the model parameters in a credible androbust manner. © 2014 by the authors.