Recent Coyote Algorithm-Based Energy Management Strategy for Enhancing Fuel Economy of Hybrid FC/Battery/SC System

Abstract

An optimized energy management strategy (EMS) based on a recent coyote optimization algorithm (COA) applied to a hybrid electric power system is proposed in this paper. The proposed hybrid system comprises fuel cell (FC), battery storage bank (BSB) and supercapacitors (SCs). The FC has been selected to be the chief power source to meet the load demand at steady state. Whereas BSB is used as the chief energy buffer and to help the FC during deficit periods and SCs are employed to meet the transient maximum power. The performance of the hybrid electric power system mostly depends on how to distribute the demanded load through different kinds of power sources. Therefore, optimized EMS is highly required to do this job. The key objective of the proposed EMS is to reduce hydrogen consumption by the hybrid system and increase the durability of power sources. To investigate the superiority and validity of COA, a comparison with other approaches is carried based on minimum hydrogen consumption and high energy efficiency. Such methods include external energy maximization strategy (EEMS), particle swarm optimizer (PSO), genetic algorithm (GA), grey wolf optimizer (GWO), grasshopper optimization algorithm (GOA), multi-verse optimizer (MVO), salp swarm algorithm (SSA) and sunflower optimization (SFO). The obtained results confirmed the superiority of the proposed COA. Using COA reduced hydrogen consumption by 38.8% compared to the EEMS method. Based on the minimum hydrogen consumption, the strategies are ranked from the best as following; COA, GWO, SSA, GOA, MVO, GA, PSO, and EEMS.