Parasitism-predation optimization algorithm-based energy management system for hybrid electric power system

Abstract

Efficient management of hybrid energy sources, including fuel cells, batteries, and supercapacitors, is crucial for optimizing fuel consumption during emergency aircraft landings. Researchers are increasingly recognizing metaheuristic optimization algorithms for their effective convergence properties. This paper introduces a Parasitism-Predation Optimization Algorithm (PPOA)-based Energy Management System (EMS) hybridized with an Equivalent Consumption Minimization Strategy (ECMS) and an External Energy Maximization Strategy (EEMS) to reduce fuel consumption in Hybrid Energy Sources (HES). The proposed PPOA-EMS is developed using MATLAB/SIMULINK and validated on the Opal-RT 5700 real-time Hardware-in-the-Loop (HIL) simulator. The PPOA-ECMS configuration achieved fuel consumption of 17.5 g in simulations and 17.44 g in real-time simulation, with an efficiency of 88.61%. Similarly, the PPOA-EEMS configuration further reduces the fuel consumption to 15.2 g in simulations and 15.36 g in real-time simulations, with an efficiency of 93.17%. This work highlights the PPOA-EMS optimization in hydrogen fuel consumption when compared to conventional and metaheuristic algorithms, including State Machine Control (SMC), External Energy Maximization Strategy (EEMS), Equivalent Consumption Minimization Strategy (ECMS), Classical PI Controller (CPI), Gray Wolf Algorithm (GWO), Mine Blast Algorithm (MBA), and Slaps Swarm Algorithm (SSA). Finally, the findings demonstrate the effectiveness of PPOA-EMS in enhancing fuel efficiency and ensuring robust energy management.