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Aircraft Energy Management System Using Chaos Red Fox Optimization Algorithm

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Abstract

In this study, the application of energy management system (EMS) for a hybrid system including fuel cell stack, supercapacitor, and battery energy storage unit in an aircraft has been analyzed. The primary purposed of using the EMS is to minimize the hydrogen consumption while controlling the supercapacitor and the battery states of charge (SOCs) under some working conditions. The minimization of the EMS in this study is based on designing a new improved metaheuristic algorithm, called Chaos Red Fox Optimization (CRFO) Algorithm to improve the system efficiency in terms of convergence and global optimization terms of view. The proposed method has been then validated by comparing with some new and popular optimization algorithms in terms of hydrogen consumption to indicate its dominance. The results showed that the proposed CRFO algorithm with 18.19 g provides the minimum hydrogen consumption toward the other comparative metaheuristic algorithms. Also, the power extracted from the battery has been kept between [− 1600, 3900] W. Furthermore, the battery SOC status is limited between 80 and 42% of constraints and the battery SOC is limited between 40 to 92%. Finally, the value of the hydrogen consumption from the suggested CRFO-based EMS are compared with two start of the art methods including traditional PI control and equivalent consumption minimization strategy (ECMS) techniques to demonstrate the recommended method’s superiority.

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Abbreviations

A :

Exponential voltage

B :

Real battery current

C sc :

Capacitance of the supercapacitor

E N :

Reversible potential

H2 :

Hydrogen

H 2 O :

Water

i :

Battery flow

i * :

Filtered battery flow

i t :

Actual battery current

I SC :

Supercapacitor module currents

I O :

Exchange current density

n e :

Number of transferring electrons

O 2 :

Oxygen

P :

Pressure

P b :

Load extracted from the battery

q f :

Flow rate of gas entry to the stack

Q :

Maximum battery volume

Q 1 :

Capacitor charge

T M :

Equivalent membrane impedance

SOC :

Battery instant State of Charge

T :

The operation temperature

\(u\) :

Fuel use agent

u s :

Desired utilization in steady-state

U SC :

Supercapacitor module voltage

v 1 :

Capacitor voltage

V act :

Activation loss voltage

V cons :

Concentration loss voltage

V T :

Terminal voltage

V Ω :

Ohmic loss voltage

V 0 :

Battery constant voltage

\(V_{min}^{DC}\) :

Minimum DC voltage for bus

\(V_{max}^{DC}\) :

Maximum DC voltage for bus

K :

Polarization constant

Δt :

Time interval

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Author information

Authors and Affiliations

  1. Department of Economics and Management, Huzhou University, Huzhou City, 313000, Zhejiang Province, China

    Zhirun Huo

  2. Department of Big Data Application and Economics, GuiZhou University of Finance and Economics, GuiYang City, 550000, GuiZhou Province, China

    SiJin Liu

  3. Department of Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

    Homayoun Ebrahimian

Authors
  1. Zhirun Huo
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  2. SiJin Liu
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  3. Homayoun Ebrahimian
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Correspondence to SiJin Liu.

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Appendix

Appendix

See Fig. 17

Fig. 17
figure 17

The two-dimensional plot of the studied case studies for: a \(f_{1}\), b \(f_{2}\), c \(f_{3}\), d \(f_{4}\)

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Huo, Z., Liu, S. & Ebrahimian, H. Aircraft Energy Management System Using Chaos Red Fox Optimization Algorithm. J. Electr. Eng. Technol. 17, 179–195 (2022). https://doi.org/10.1007/s42835-021-00884-5

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  • DOI: https://doi.org/10.1007/s42835-021-00884-5

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