A Comparative Study of State of Charge Estimation Methods of Ultracapacitors for Electric Vehicles Considering Temperature Characteristics

Abstract

Accurate ultracapacitor (UC) state of charge (SOC) estimation is one of the most important tasks of hybrid energy storage systems. This paper systematically compares and evaluates three types of UC SOC estimation algorithms in terms of accuracy, robustness and self-correction ability under different ambient temperatures. They are the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF) and unscented Kalman filter algorithm (UKF), respectively. Firstly, the parameters of UC model are identified by genetic algorithm under the hybrid pulse power characteristic test at -10 °C, 10 °C, 25 °C and 40 °C. Next, the polynomial fitting is employed to determine the relationship between parameters and temperatures, and the temperature-varied model is established. Then, the implementation procedures of these three algorithms for UC SOC estimation are designed. Finally, the results indicate that the AEKF algorithm is superior to the EKF and UKF. The average absolute error and root mean square error of the AEKF algorithm are within 0.227 % and 0.318 % at different temperatures, respectively. In addition, the UC model with variable temperature characteristics and conventional UC model are further compared in terms of UC SOC estimation. The results show that the proposed model has a wider application.

Keywords: ultracapacitorselectric vehiclesSOC estimationtemperature-varied model