Online Battery Pack State of Charge Estimation via EKF-Fuzzy Logic Joint Method

Abstract

Hybrid Electric Propulsion System (HEPS) is attracting growing interest from researchers and other stakeholders working in the field. The pace of technology development is accelerating due to pressures for more energy efficient air vehicles with lower emissions and environmental impact; and to meet ACARE 2050 targets. The battery pack State of Charge (SOC) plays a critical role in the HEPS supervisory controller. In this paper, firstly a new operation-classification battery model is proposed for Li-Po battery. Moreover, since the accuracy of parameter identification is important in state estimation. An event triggered Adaptive Genetic Algorithm (AGA) is used for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Furthermore, based on maximum and minimum battery cell voltages and SOC values, a Fuzzy Logic Estimator (FLE) is used for pack SOC estimation. Experimental results show that the proposed AGA can effectively track battery parameter variation and the SOC estimation error for single cell and for the complete battery pack with less than 1% error.