4.7 Article

An improved energy management strategy for fuel cell/battery/supercapacitor system using a novel hybrid jellyfish/particle swarm/BAT optimizers

Journal

JOURNAL OF ENERGY STORAGE
Volume 57, Issue -, Pages -

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ELSEVIER
DOI: 10.1016/j.est.2022.106276

Keywords

Energy management; Fuel cells; Hybrid optimization; Hydrogen minimization; Stress reduction

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This paper presents a fuel cell hybrid emergency system composed of fuel cells, batteries, and supercapacitors effective for different conveyance applications. The key factors of each energy management strategy (EMS) are reducing hydrogen consumption and prolonging power source lifetime. An incipient EMS based on an optimized proportional-integral (PI) controller is proposed to ensure the operation of the fuel cell stack within its maximum efficiency region. JSPSOBAT, a hybrid metaheuristic optimization technique, is used to tune the PI controller gains and achieve better performance compared to other EMS strategies.
In this paper, a fuel cell (FC) hybrid emergency system is presented. It contains fuel cells, batteries, and supercapacitors which are effective for different conveyance applications. Abbreviating the hydrogen consumption and incrementing the lifetime of power sources are the key factors of each energy management strategy (EMS). An incipient proposed EMS depending on an optimized proportional-integral (PI) controller is presented considering FC efficiency to ensure the operation of the FC stack within its maximum efficiency region and reduce the stress on it which in turn leads to reducing its hydrogen consumption. Regarding tunning the PI controller gains (Kp,Ki), a proposed hybrid metaheuristic optimization technique named JSPSOBAT has been presented which merges jellyfish (JS) optimizer, particle swarm optimizer (PSO) and BAT optimizer to achieve the balance between exploration and exploitation phases. To firstly validate the effectiveness of the JSPSOBAT technique, a comparative study with other single and hybrid metaheuristic optimization techniques is presented by testing on 50 complex benchmark functions. Then, the JSPSOBAT is used to select the controller gains of the proposed PI controller. A 30 min load profile of a More Electric Aircraft (MEA) is used. The performance of the proposed EMS using the proposed optimization technique is compared with other EMS such as state machine control strategy, classical PI control strategy, equivalent consumption minimization strategy, external energy maximization strategy, and the results show that the proposed technique produces the best performance.

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