Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle

被引:7
|
作者
Ferahtia, Seydali [1 ]
Rezk, Hegazy [2 ,3 ]
Ghoniem, Rania M. M. [4 ]
Fathy, Ahmed [5 ,6 ]
Alkanhel, Reem [4 ]
Ghonem, Mohamed M. M. [7 ]
机构
[1] Univ Msila, Dept Elect Engn, Lab Genie Elect, Msila 28000, Algeria
[2] Prince Sattam bin Abdulaziz Univ, Coll Engn Wadi Alddawasir, Dept Elect Engn, Wadi Alddawasir 11991, Saudi Arabia
[3] Minia Univ, Fac Engn, Elect Engn Dept, Al Minya 6111, Egypt
[4] Princess Nourah bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, Riyadh 11671, Saudi Arabia
[5] Jouf Univ, Fac Engn, Elect Engn Dept, Sakaka 72388, Saudi Arabia
[6] Zagazig Univ, Fac Engn, Elect Engn Dept, Zagazig 44519, Egypt
[7] Mansoura Univ, Fac Engn, Dept Comp, Mansoura 35516, Egypt
关键词
hybrid electric vehicles; energy management; energy efficiency; fuel cells; hydrogen; FUEL-CELL; POWER-SYSTEM; STRATEGY; BATTERY; ECMS; HEV;
D O I
10.3390/su15043267
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor's required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 degrees C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC.
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页数:19
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