An energy management scheme for improving the fuel economy of a fuel cell/battery/supercapacitor-based hybrid electric vehicle using the coyote optimization algorithm (COA)

Abstract

This study describes a multi-input power system that is suited for fueling electric automobiles, InterCitys, and airplanes, particularly in situations with significant fluctuating load demand. The dual framework utilizes fuel cells (FC), batteries, and super capacitors (SCs). An energy management system (EMS) remains a critical aspect of lowering overall hydrogen consumption and minimizing the degradation of FC functionality. A novel EMS that has been suggested focused on a novel optimization method known as the Coyote optimization algorithm (COA), and it considers the fact that the total load is adequately supplied within the limitations of each power source. To minimize the hydrogen consumption. By maximizing the power generated by the energy storage devices, the energy acquired from the FC is reduced. In comparison to other optimization methods, the COA would be a practical, effective, and relatively straightforward optimizer that only involves a limited number of controlling factors to be set. The framework application MATLAB/Simulink is used to create the proposed method. In order to show the effectiveness of the proposed methodology, a study with several different conventional techniques is performed, which includes the classic proportional-integral control mechanism, the frequency decoupling with state machine (FDSM) controlling technique, the equivalent consumption minimization scheme (ECMS), and the external energy minimization scheme (EEMS). The efficacy of the algorithm and the FC’s aggregate H2 usage serve as the focal points for comparison in this work. The outcomes demonstrate that the recommended COA strategy is superior and more effective than the alternative approaches.