Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter

Abstract

Ultracapacitors (UCs) are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important 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, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST) on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner.

A comparative study of equivalent circuit models of ultracapacitors for electric vehicles

“…Electric vehicles (EVs) are widely accepted as an integral part of such a green energy system provided they use electricity generated by a renewable energy source [2]. Energy storage systems (ESSs) play decisive roles in defining the performance of electric vehicles, since drivability, braking recuperation capacity and driving range are highly dependent on the specific power and energy of the ESS in a vehicle [3].…”

Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise

“…System identification has a significant effect on the filtering [1][2][3], state estimation [4][5][6], system control [7][8][9] and optimization [10]. For example, Scarpiniti et al proposed a nonlinear filtering approach based on spline nonlinear functions [11]; Zhuang et al presented an algorithm to estimate the parameters and states for linear systems with canonical state-space descriptions [12]; Khan et al discussed the theoretical implementation of robust attitude estimation for a rigid spacecraft system under measurement loss [13].…”

Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

“…To ensure the model accuracy under various operational conditions, the online parameter estimation should be carefully addressed. The use of a dual Kalman filter is an efficient approach to estimate the system states and model parameters simultaneously [44,45]. However, to the author's knowledge, such a method has not been investigated for FOC-based models, especially for order identification.…”