battery system

international journal of hydrogen energy xxx (xxxx) xxx

Available online at www.sciencedirect.com

ScienceDirect journal homepage: www.elsevier.com/locate/he

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system Hegazy Rezk a,b,*, Ahmed M. Nassef a,c, Mohammad Ali Abdelkareem d,e,f,**, Abdul Hai Alami d,e, Ahmed Fathy g,h a

College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia Electrical Engineering Dept., Faculty of Engineering, Minia University, Egypt c Computers and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Egypt d Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates e Center for Advanced Materials Research, University of Sharjah, PO Box 27272, Sharjah, United Arab Emirates f Chemical Engineering Department, Faculty of Engineering, Minia University, Egypt g Electrical Engineering Department, Faculty of Engineering, Jouf University, Saudi Arabia h Electrical Power and Machine Department, Faculty of Engineering, Zagazig University, Egypt b

highlights
A comprehensive comparison among nine energy management strategies is done.
Hydrogen fuel economy and overall efficiency are used as a metrics of comparison.
Salp swarm optimization based strategy is best one among others.
The minimum consumed hydrogen is 19.4 gm.
The maximum efficiency is 85.61%.

article info

abstract

Article history:

The aim of this study is to introduce a comprehensive comparison of various energy

Received 15 September 2019

management strategies of fuel cell/supercapacitor/battery storage systems. These strate-

Received in revised form

gies are utilized to manage the energy demand response of hybrid systems, in an optimal

10 November 2019

way, under highly fluctuating load condition. Two novel strategies based on salp swarm

Accepted 26 November 2019

algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these stra-

Available online xxx

tegies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimiza-

Keywords:

tion, maximization, external energy maximization, and equivalent consumption minimi-

Energy management

zation. Hydrogen fuel economy and overall efficiency are used for the comparison of these

Optimization

different strategies. Results demonstrate that the proposed SSA management strategy

* Corresponding author. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia. ** Corresponding author. Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates. E-mail addresses: [email protected] (H. Rezk), [email protected] (M.A. Abdelkareem). https://doi.org/10.1016/j.ijhydene.2019.11.195 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

2

international journal of hydrogen energy xxx (xxxx) xxx

Fuel cell

performed best compared with all other used strategies in terms of hydrogen fuel economy

Supercapacitor

and overall efficiency. The minimum consumed hydrogen and maximum efficiency are

Battery

found 19.4 gm and 85.61%, respectively.

Hydrogen consumption

© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction With the rapid increase in the world population, humans are seeking to build their strongest integrated societies that would cater to their constituents by providing them with fundamental services. These services range between industrial, medical, civil, as well as many other special scientific and modern technology applications to make our life easier. All these items lead to a staggering increase in energy demand, and most of the supply relies on fossil fuel combustion. The depletion of these fossil fuels, oil and gas price fluctuations and climate changes, are pushing the world to consider alternative sources of energy that are sustainable and ecofriendly. Renewable energy sources (RES) such as wind, solar, biomass, ocean, geothermal, hydro … etc. are considered among the best candidates for that ambitious takeover. Fortunately, the energy obtained from these different types of RESs is not only sustainable but also environmentally friendly compared to that obtained from fossil fuels. Fuel cells are electrochemical devices that used for direct conversion of chemical energy of fuels into electricity with high efficiency [1e4] that can be fed with different renewable energy resources [5e7]. Hydrogen fuel is considered as one of the clean energy sources without pollutants or harmful emissions which is used in the highly efficient fuel cell (FC) in the energy applications and transportation [8]. Successful hybrid power generation systems are those containing FC power systems supported by energy storage units such as batteries and supercapacitors [9]. Batteries (BS) and supercapacitors (SC) are used to improve the performance of the whole system. The battery has the advantage of supplying an average power during the load variation. However, the supercapacitor is used due to its big advantage of fast charging/discharging cycles. The hybrid FC/BS/SC arrangement can be used as an electrical backup system for the special and critical power supplies that require an uninterrupted power supply all the time like space vessels, nuclear plants and crucial control processes. In such hybrid systems, energy management strategy (EMS) is required to distribute the load's demand between the different energy sources to achieve the highest economy of fuel consumption taking into consideration that the energy sources are operating within their specified limits, without ignoring the impact of the EMS on the hybrid power system life cycle. Several researchers have used different energy management strategies such as rule-based fuzzy logic, classical PI controller, Fourier transform techniques, and others [10] in hybrid energy systems. Many schemes have been proposed for the energy management of the FC hybrid systems that include some reported comparative studies and the resulting performances have been reviewed and mentioned in this

work. Li and Liu proposed an optimized fuzzy logic control (FLC) to distribute the power demand between the FC and the battery of a hybrid electric vehicle. The overall system efficiency is maximized by determining the best degree of hybridization (DOH) and the power management strategy. Their simulation results confirmed that the FLC of the hybrid vehicle provides good results in both of the fuel consumption and the overall system efficiency [11]. Caux et al. presented a FLC of the power flow on a FC hybrid vehicles (FCHV) to obtain a minimum hydrogen consumption. The controller's gains are online optimized using genetic algorithm (GA) to decrease the consumption amount in a predefined driving cycle [12]. Li et al. proposed a FLC for designing an EMS comprises (FC þ battery) and (FC þ Battery þ SC) hybrid vehicles. Their simulation results showed that the proposed FLC method is satisfying the power demand of four regular driving cycles and it is succeeded to distribute the energy demands among the energy supplies [13]. Mallouh et al. reported that a fine tuned FLC is competitive to fuel cell load following strategy (FCLS) and they are equivalent in terms of the power distribution and the fuel consumption. They used neural networks to optimize the FLC parameters. The control strategies are applied to electric auto rickshaw and it produced better performance [14]. Hemi et al. presented a real-time FLC technique to design an EMS for a hybrid electric vehicle. This methodology planned to keep the battery from overcharging during the repeated braking energy accumulation. Their work studied the performance of three arrangements, FC/Battery, FC/SC and FC/Battery/SC during real-time driving situations while the driving cycle is unknown. The proposed FLC strategy satisfied the energy demand for the unknown driving cycles. Their results showed that the FC/Battery/SC configuration increases the battery lifetime [15]. Zhou et al. [16] proposed online energy management control strategy based on a fractional-order extremum seeking (ES) method for fuel cell hybrid electric vehicles. The fractional-order ES demonstrated faster convergence speed and higher robustness compared with the traditional integer-order ES. Mohammedi et al. introduced a passivity-based modeling and control using FLC strategies of hybrid DC link equipped with FC and SC as primary and secondary power source, respectively. They applied a FLC strategy to control a DC hybrid power sources system. This controller is able to estimate the desired current of SC based on the SOC of the SC and the FC remaining hydrogen quantity (QH2). Their simulation results are verified and validated for the proposed strategy which demonstrated that it has robust dynamic characteristics [17]. FLC was used to improve the performance and life time of a hybrid FC/battery (Li-ion) system that is used in an electrical Electric Vehicle (HEV) [18]. The load power, power error

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

international journal of hydrogen energy xxx (xxxx) xxx

and state of charge (SOC) of the battery are used as the inputs of the FLC, while the FC output current is the output. Keeping work within the applied constrains, the EMS distributed effectively the power between the fuel cell and the battery [18]. Pachauri and Chauhan presented a review study on the modeling and EMS of fuel cells (FCs) technologies. These technologies advantages/disadvantages and applications are highlighted. Control methodologies of EMS with simple and easy to implement PI controller for a phosphoric acid FC (PAFC) operation are proposed. Their method's performance is found to be adequate even under dynamic conditions [19]. Bassam et al. developed an improved PI controller-based EMS for a hybrid FC/Battery passenger vessel. The performance of their proposed PI-based EMS is found competitive to the original PI and other EMS. Their strategy showed a significant hydrogen saving compared to other strategies [20]. Pachauri and Chauhan extended their work to analyze the PAFC of EMS with different PI control methodologies. Their proposed control schemes' simulations showed satisfactory performances under various conditions [21]. Amrouche et al. presented a hybrid electric vehicle system using a proton exchange membrane FC (PEMFC) coupled with SCs with high instantaneous power variations. Power management is achieved by using a PI controller at bus based on passivity. Their modeling performance in different operating conditions is analyzed [22]. Bassam et al. [23]. used different EMS such as equivalent consumption minimization (ECM), chargedepleting charge sustaining, state-based, and classical PI controller for increasing the energy performance efficiency of a passenger ship that uses a FC/Battery propulsion system. Considering eight working hours per day, a maximum energy of 8% and an H2 consumption of 16.7% were achieved in case of the proposed multi-scheme strategy [23]. Fernandez et al. have developed a hybrid system that contains FC and Ni-MH BS integrated with a DC-DC converter for providing the power demand from the tramway loads and SMCS is employed for providing the power demand by a certain drive cycle [24]. The obtained performances of the real driving cycle of the tramway check the sufficiency of the proposed framework. A various leveled prescient control technique to upgrade both power usage and oxygen control at the same time for a hybrid FC/SC framework is presented by Chen Q. et al. [25]. The control utilizes fuzzy clustering-based modeling, constrained model predictive control, and adaptive exchanging among multiple models. The oxygen starvation is taken into consideration via trading off transient current demand from FC to SC. Zhang et al. [26] introduced a wavelet-transform technique for identifying and allocating various frequency energy demands to accomplishing sources for achieving an optimal power dispatching control algorithm. Another work is done by Changjun Xie et al. [27], where they established a system comprising FC/BS hybrid powertrain to test and validate their proposed control methodology and the system's elements as a hardware-in-the-loop test platform. They have also introduced a strategy of power management for minimizing the fuel cell stack H2 consumption with limited power rising rate. Liu et al. introduced a power management methodology with decreasing the frequent regulation of the FC's stack variables. This was done by decoupling the FC's stack from the transient load power

3

requirement. As a result, the FC's stack and the load can independently be managed [28]. Equivalent consumption minimization strategy (ECMS) has been applied for reducing the consumed hydrogen and thus enhancing the response of a hybrid system composed of proton exchange membrane fuel cell (PEMFC), battery and ultracapacitor used for powering tram [29]. Han et. al. presented a work to optimally adapt the equivalent factor of ECMS via dynamic programming for improving the fuel economy for a fuel cell HEV. In Refs. [30e32], a model of hybrid fuel cell/battery has been built in Matlab/Simulink for supplying a tramway and the ECMS has been utilized for managing the system energy in an optimal way. Rule-based ECMS used with vehicles has been presented in Refs. [33,34] for enhancing the conventional ECMS. The strategy is based on only one design parameter that does not need any further tuning but the presented model is so complex which requires very long computational time. Fletcher et al. [35] used a stochastic dynamic programming algorithm for solving the degradation problem of PEMFC. In a hybrid system composed of FC as the main source and SC as an additional source in peak powers, Li et al. [36] optimized the performance of the hybrid energy system using ECMS where FC penalty factor of efficiency and SOC coefficients of SC and battery are used as the design variables. With their proposed strategy, the performance of the ECMS is increased. Incorporating ECSM with a feed-forward predictive scheme [37], or with fuzzy proportional integral controller [38] resulted in controlling its equivalent factor and thus improving its performance. The power fluxes of FCHEV were managed using dynamic model of the FCHEV and neural network as in Ref. [39]. The optimized dynamic model was utilized for feeding and training the neural network. Caux et al. [40] presented a combinatorial approach to optimize the hydrogen consumption in hybrid electric vehicle composes fuel cell and supercapacitor. The HEV has been presented as a linear model, which was solved by integer linear programming approach. The authors used the cutting planes in the model to reduce the search space and speed up the optimization process. In Ref. [41], a powertrain with fuel cell as primary power source and two secondary sources, batteries and supercapacitors has been modeled with Matlab/ Simulink, multi-objective genetic algorithm. GA has been applied to optimize the configuration of vehicles and to control the HEV such that minimizing the total hydrogen consumption by the system. Tiar et al. [42] presented a fuzzy-logic control strategy for managing the power of a small-scale hybrid power supply of photovoltaic (PV) and fuel cell. The PV is controlled via fuzzy-logic maximum power point tracker (MPPT) for tracking the maximum power while the fuel cell is covering the rest of load. Additionally, the inverter is controlled via back stepping algorithm. Different approaches of incorporating hydrogen energy technology in a hybrid energy system, fuel cell sizing methodologies and optimization approaches of hybrid system energy management have been reviewed in Ref. [43]. Chen et al. [44] optimized multi-objective function of the main cost, efficiency of electricity and power source reliability of hybrid power supply composes wind turbine, solar panel and fuel cell via Hammersley sequence sampling approach. Comparison of different energy

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

4

international journal of hydrogen energy xxx (xxxx) xxx

management approaches of FCHEV have been done with applying extremum seeking controller such that decreasing the hydrogen usage [45]. It is expected that an EMS will be applied in the near future for estimating the energy requirement of the power-train using hybrid FC/SC to improve the fuel economy [46]. In Refs. [47e49] a strategy for energy management employed for electric vehicles have been presented. It is based on the communication between the later and the buildings. Falh [50], presented energy management strategy based on fuzzy logic to enhance the power extracted from a hybrid system comprising generator, consumption engine and battery. Table 1 summarizes the various EMSs that are mentioned previously. The main objective of this research is to present a comprehensive comparison of different EMSs that applied to FC/SC/BS hybrid system. Proton exchange membrane FC type feed by Hydrogen as a fuel is considered. EMSs are used to optimally manage the energy in the hybrid FC/SC/BS system under highly fluctuated load condition. According the No Free Lunch theorem [51], no single optimizer can solve all optimization problems, which means that new optimizers are still recommended in the research area of energy management. The main strength of the proposed work is applying recent and efficient metaheuristic optimization algorithms called salp swarm algorithm (SSA) and mine blast optimization (MBO). Thanks to modern optimization, the efficiency can be increased and the hydrogen consumption reduced compared to other conventional strategies. EMSs based on modern optimization are compared with commonly strategies like fuzzy logic control (FLC), PI classical control, equivalent consumption minimization strategy, state machine strategy, maximization strategy, equivalent consumption minimization strategy (ECMS), and external energy maximization strategy (EEMS). The key factors of comparison are the hydrogen fuel consumption and the system's overall efficiency. For better EMS, the fuel consumption should be minimum and the overall efficiency should be maximum.

Hybrid FC/BS/SC system components Fuel cell There are several advantages of using FCs as a power source such as their high efficiency, compact size, silent or low noise operation and eco-friendliness. Despite all the above merits, FCs are known to have some undesirable properties such as; its response to the load variations is slow; its output voltage is unstable; its lifetime is short because of the increase in the rippling current; and finally it is somehow expensive [60]. Activation, ohmic, and concentration losses are the common losses in FCs. In the following, the linear and nonlinear model for the PEMFC for the stack voltage, anode flow, and membrane hydration are explained and the modeling and simulation of the FC is discussed. By analyzing the FC operation, the next equations could be represented [61]: ENernstt ¼ 1:229 e 8:46 x 104 ðTe 298:15Þ

pffiffiffiffiffiffiffiffiffi  þ 4:31 x 105 T ln PH2 PO2

(1)

where T is the temperature, PO2 and PH2 are the oxygen and hydrogen pressures, respectively. For a single FC, the overall voltage will be expressed as follows; VFC ¼ ENernst  Vact  Vohmic  Vcon.

(2)

Where ENernst is the raw output voltage generated from the chemical reaction, Vact represents the activation over potential voltage, Vcon represents the mass concentration voltage drop, and Vohmic represents the ohmic voltage drop. These voltage losses could be listed as follow: Vact ¼  [x1 þ x 2 T þ x 3 T $ ln (CO2) þ þ x

4

T $ ln (iFC)]

(3)

Vcon ¼ B $ ln (1 e J/Jmax)

(4)

Vohmic ¼ iFC $ (RM þ RC)

(5)

Where x1, x2, x3, x4, and B are the FC's parameters, CO2 is the oxygen concentration, iFC is the FC current, J is the current density, RM is the membrane resistance and RC is the resistance related to the proton's transportation through the membrane. The overall equivalent circuit for the FC is reported in Ref. [62]. The I-V curve of the FC could be represented by the relation between the current density and the FC's voltage.

Batteries and supercapacitors A battery could be used for energy storage in a stand-alone power system like the one used in automotive applications. Moreover, the battery can be considered as a core element in the hybrid electrical power system with PV, FC, and SC to obtain an adequate response for the overall power system. The SC is used in conjunction with the battery to attenuate the peak current in the battery in case of a very high fluctuation in the load demand is occurred. Therefore, because of its highefficiency cycle (approximately 100%), FC is the appropriate power source and storage energy in case of repeated charge/ discharge cycles compared to the battery which used to supply the average power needed. In other words, it has a faster power delivery as well as the charge/recharge cycles are more than the battery. The two main functions of the battery are explained in Ref. [63]. The high energy density and low power density characteristics of the batteries and the effects of the internal resistance on the charging/discharging current are also discussed. Cabrane et al. [64] explained a power system using batteries and SC as an energy storage with PV power system. The use of SC to overcome the disadvantages of the batteries was studied. Some parameters like battery root main squared (RMS) current gain performance, energy loss gain and load power surge were also explained. Comparisons between the properties of the batteries and supercapacitors are presented in Table 2 [65]. In modeling a Li-ion battery, the battery's voltage, Vbatt, can be formulated as follows: Vbatt ¼ E0  K

Q Q * it  Rb i þ Ab eðB:itÞ  K i Q  it Q  it

(6)

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

Ref.

Main Sources

Application Time System Location SC Rating (F) BS, rating, FC, rating, (sec) Capacity (Ah) (KW) (KW) 660

e

64

127

Turkey

45

40

260

Tramway

[53] FC, BS, SC

Tramway

[25] FC/SC

Car

600

50

[12] FC, BS, SC

Aircraft

1800

14

Canada

88

40

12.5

[54] FC/SC

Vehicle

190

1

France

125

e

1.2

1

China

[28] FC/BS

[55] FC/SC

Vehicle

[56] SC/BS

Vehicle

1200

[29] FC, BS, SC

Tram

[57] FC, BS

[30] FC, BS

400

Spain

[52] FC, BS

86

75

Remarks

State machine cascade control

Simulation only

model predictive control

Experimental and simulation

Classical PI Control Frequency Decoupling Rule-based fuzzy logic equivalent consumption minimization Frequency Decoupling

Experimental and simulation

Experimental

Frequency Decoupling

Experimental and simulation

1.26

frequency decoupling

Simulation

frequency decoupling Equivalent consumption minimization strategy Equivalent consumption minimization strategy Equivalent consumption minimization strategy Equivalent consumption minimization strategy

Experimental

China

1

e

Japan

64

42

e

3500

560

China

130 KW

60

150

FCHEV

800

e

United States

e

e

75

Tramway

660

400

Spain

e

34

200

10000

400

e

e

e

e

[31] Engine, Motor, BS HEV

Strategies

Simulation and Experiment

Advantages

Disadvantages

New topology of dcedc converter is presented Respond to the rapid changes in the demand Enhance the transient performance of the hybrid system Used online energy management strategy

Require excess effort in execution Complicated structure of EMS

New one converterbased EMS is presented Decrease state variables of FC frequent regulation Stability of the EMS is considered and assessed The constructed EMS is simple Enhance drivability and economy

Complicated EMS structure Needs excess data with time consuming

Need excess effort in execution Large consuming time Model of the presented system is complicated Need high initial SOC for battery and SC High SOC level for FC and SC

Simulation only

Improves economy of Excess effort for fuel changing EF trajectory

Simulation only

No need to charge battery at the end of the cycle

Simulation only

Mitigate the extensive The EMS adapting of the ECMS construction is equivalence factors complicated

international journal of hydrogen energy xxx (xxxx) xxx

Need excess data with time consuming

(continued on next page)

5

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

Table 1 e Summary of various EMSs.

Main Sources

Application Time System Location SC Rating (F) BS, rating, FC, rating, (sec) Capacity (Ah) (KW) (KW)

Strategies

Remarks

[32] FC, BS

Tramway

400

e

e

e

105

150

[34] FC, BS

Powertrain

e

75

Japan

e

1.5 kWh

45

[35] FC, BS

FCHEV

3000

4.93

e

e

2 kWh

4.8

[36] FC, BS, SC

FCHEV

1408

10

e

e

30

5

[37] FC, BS

FCHEV

e

75

United States

e

9

75

[38] Engine, Motor, BS HEV

600

150

e

e

70

e

[58] FC, BS, SC

FCV

e

6

e

e

e

6

[59] FC

PEMFC

12

6

e

e

e

6

[39] FC, BS

FCHEV

1400

75

e

e

5

25

Neural networks

Simulation only

[40] FC, SC

FCHEV

1400

e

e

e

e

24

Simulation only

[41] FC, BS, SC

Powertrain

e

60

e

45Ah

2

e

Integer linear programming approach Multi-objective genetic algorithm

[42] PV, FC

Hybrid energy 20 systems (HES)

0.268

e

e

e

0.09

[45] FC, BS

FCHEV

1400

e

e

e

10 kWh

30

[46] FC, SC

Powertrain

520

e

e

165

e

1.2

Equivalent consumption minimization strategy Equivalent consumption minimization strategy Equivalent consumption minimization strategy Equivalent consumption minimization strategy Equivalent consumption minimization strategy Adaptive equivalent consumption minimization strategy Global extremum seeking algorithm global extremum seeking algorithm

Advantages

Disadvantages

Simulation only

Simple constructed ECMS

Needs a long simulation time

Simulation only

Find the relation between the fuel consumption and battery SOC Avoid FC transient loading

Need excess data with time consuming

Simulation only

Simulation only

Neglect the battery damage for transient loads

Battery SOC and it is implementable online at low burden is considered The EMS is extendible to different system topologies

Very complicated and require large efforts

Simulation only

The EMS is robust

Require excess data

Simulation only

Both FC net power and the its Consumption Efficiency are considered Simple EMS based ANN Improve the EEMS quality of decisions

The presented optimizer may fall in local optima

GA may fall in local optima

Fuzzy logic-based Experiment only power management approach Extremum seeking Experiment only algorithm

Minimize H2 consumption and sustaining battery SOC Simple energy management strategy based on Fuzzy-logic Energy management strategy is online

Estimation of short- Simulation and term future energy Experiment demand

Economy of fuel and drivability are considered

Simulation only

Simulation only

Simulation only

Need excess data for simulation

Require excess data for training ANN Large consuming time

Need excess data

Need excess data with time consuming Complicated construction

international journal of hydrogen energy xxx (xxxx) xxx

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

Ref.

6

Table 1 e (continued )

international journal of hydrogen energy xxx (xxxx) xxx

7

Table 2 e Comparison of batteries vs. supercapacitors. Item Charge/discharge cycles (service life) Charge/discharge time Energy density Power density Cost

Batteries

Supercapacitors

Low charge/discharge cycle number Slow and steady energy supplier for large energy demands High energy density Low power density Low cost

Enormous of charge/discharge cycles without losing energy capability Fast performance at the starting and transient conditions

where E0 is the battery constant voltage (V), K is the polarization constant V/(Ah), Q is the battery capacity (Ah), i* is the filtered battery current (A), it is the actual battery charge (Ah), Ab is the exponential zone amplitude (V), B is the exponential zone time constant inverse (Ah)1 and Rb is the battery internal resistance (U). The capacitance of SC is represented as in the following eqn.: 31

2 6 d 6 þ C¼6 4Ne εε0 Ai

7 !7 7 5 c pQffiffiffiffiffiffiffiffiffiffiffi ffi

2Ne RT FQc sinh

N2e Ai

(7)

8RTεε0 c

where, Ne denotes the number of layers in the electrode. є0 and є are the permittivities of the free space and the electrolyte material (F/m), respectively. Ai refers to the joint area between electrodes and electrolyte (m2). d denotes the Helmholtz layer length (or molecular radius) (m). Qc denotes the cell electric charge (C). c denotes the molar concentration (mol m3). The overall capacitance, CT, of the SC can be calculated as, CT ¼

Np C Ns

(8)

where, Ns and Np are the series and parallel cells of the SC's module, respectively. The output voltage of the SC, taking into consideration the resistive losses, is represented as in the following eqn.: VSC ¼

N p Qc  RSC iSC CT

(9)

where RSC and iSC are resistance (U) and current (A) of the SC's module, respectively.

Proposed energy management strategies The following sections highlight the proposed methodologies used for energy management of FCs.

Fuzzy logic control (FLC) method Fuzzy logic (FL) is firstly proposed by Zadeh in the 1965 [66] and it is considered as an extension to the traditional binary logic. The binary-valued logic deals only with binary values of 0s or 1s while FL is a multivalued logic. Therefore, the binary logic can be thought of as a special case of FL. FL represents events in a similar way to that of the human. For example, in binary logic, temperature has just two conditions, i.e., hot above

Low energy density High power density High cost

certain temperature and cold below certain temperature, while in FL temperature can be very cold, cold, normal, hot, very hot, etc. Moreover, FL can deal with cases of uncertain, ambiguous, and noise data. Since the discovery of FL, it becomes one of the most important tools in modeling and control. FL consists of three sequential stages or phases, namely; fuzzification, interface system which contains the rule-base system, and defuzzification processes. Fuzzification step is responsible for converting the input's value from the normal status to its mapped fuzzy value. The mapping of each variable (either input or output) is done through fuzzy membership functions (MFs) to describe the degree of belonging of each of these variables to those MFs in the range [0 1]. The value of one denotes full belonging to such MF while zero implies that this variable's value is not belonging to this MF at all. There are two forms of fuzzy rules; the first is Mamdanitype [66] and the second is Takagi-Sugeno-Kang type (TSK) [67]. An example of a Mamdani-type fuzzy model that comprises 2-input one-output system can be formulated as: IF a is Low and b is Medium THEN c is High. While the TSK type fuzzy rule takes the form: IF a is Low and b is Medium THEN c ¼ g (a, b).where, the system's inputs are represented by a and b and the system's output is represented by c. Low, Medium and High are representing the membership functions while g (a, b) refers to the rule's output function. In this work, the load demand and SOC have been assigned as fuzzy inputs whereas its output is the FC current. As demonstrated in Fig. 1, the load power, the SOC and the FC power are represented by 5, 3 and 5 trapezoidal MFs, respectively. Mamdani rule-base type is adopted and the rules are listed in Table 3. The “max-min” and COG are assigned for the inference and the defuzzification methods, respectively. Then the FC output current (IFC) is obtained based on the knowledge of the PFC, which is evaluated via the FLC and FC voltage (VFC).

External energy maximization strategy (EEMS) In hybrid FC/BS/SC, it is important to optimally manage the energy between the energy elements for enhancing the hybrid system performance. This job can be achieved by reducing the hydrogen consumption in the proposed system with maintaining SOC of both BS and SC within their acceptable limits. The hydrogen consumption minimization methodology requires a complete model of fuel cell consumption which is a very tough process. EEMS is used for reducing the usage of H2 through increasing the demand of the BS and the SC while

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

8

international journal of hydrogen energy xxx (xxxx) xxx

Fig. 1 e The membership functions for the FC-FLC system inputs and output.

Table 3 e FC system fuzzy rules (Mamdani-type). SOC

Pload

Very Low Low Medium High

High

Medium

Low

Very Low Low Medium High

Very Low Low Medium High

Low Medium High High

working within their operating conditions. EEMS is characterized by its simplicity as it requires only the cost function of the BS and the SC and it doesn't require the calculation of battery energy that is usually determined empirically [10]. The configuration of the EEMS is illustrated in Fig. 2. It can be seen from the figure that the EEMS algorithm inputs are the voltage of the DC bus and the BS's SOC or SC's SOC, while reference power of BS and SC charge/discharge voltage (DV) are the outputs. The reference power of the FC (via the FC current (IFC*)) is obtained by comparing the load

power and the BS power. The Charge/discharge state of the SC is determined by comparing the actual DC bus voltage with the sum of the SC's voltage and the DC bus reference voltage (Vdc_ref). The following block diagram describes the mathematical optimization formula of the EEMS: In the EEMS optimization problem, battery power and charge/discharge voltage of the supercapacitor; x ¼ [Pbatt, DV], have to be evaluated. The objective function to be maximized is the energy provided via the SC and the battery within a definite time interval, it can be described as follows [10]: Maximize   1 J ¼  Pbatt :DT þ Cr :DV2 2

(10)

In the current optimization process, an inequality constraint of the objective function based on the energy supplied by battery is found. This constraint is formulated as follows: 
Pbatt DT  SOC  SOCmin Vbatt Q

(11)

Fig. 2 e EEMS configuration. Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

international journal of hydrogen energy xxx (xxxx) xxx

While the parametric inequality constraints of battery power and DC bus voltage are described as follows: max Pmin batt  Pbatt  Pbatt

Vmin  Vdc  Vmax dc dc

(12)

where Pbatt is the battery delivered power during sampling time of DT, Cr is supercapacitor rated capacitance, Vmin dc is the is the minimum limit of the DC bus voltage while Vmax dc maximum one, Vbatt is the nominal voltage of the battery and Q is rated capacity of the battery.

State machine control strategy The block diagram for the state machine control strategy (SMCS) is illustrated in Fig. 3. The SMCS can be implemented with eight states [10]. Through the knowledge of the SOC of the battery and the load demand, the FC power can be calculated. The output of SMCS technique is the FC reference power. Then, the FC reference current is obtained via dividing the SMCS's output by both the FC voltage and the boostconverter efficiency. The most essential drawback of the SMCS is that a hysteresis control is needed when switching the states from one to another [10]. This issue affects the EMS response to any changes in the load demand.

9

Equivalent consumption minimization strategy (ECMS) The ECMS aims at reducing the fuel consumption in the FC. In this strategy, a variable equivalence factor that is based on the battery's SOC is used. Moreover, considering the equivalence factor as a part of the objective function, which is required to be optimized, makes the ECMS less sensible to the SOC balance coefficient (m). In this case, the objective function of the optimization problem can be formulated as in eqn. (15) [10]: Min   F ¼ Pfc þ ap Pbatt  DT

(15)

The equality constraint of the optimization problem is as follows: Pload ¼ Pfc þ Pbatt ap ¼ 1  2m

(16)

ðSOC  0:5ðSOCmax þ SOCmin ÞÞ SOCmax þ SOCmin

(17)

While the decision variables boundaries are described as the following:  Pfc  Pmax Pmin fc fc (18)

max Pmin batt  Pbatt  Pbatt

0  ap  2

Proportional-integral (PI) method Despite PID controller is used in many industrial and power management applications but PI controller is still very popular in such applications as well. This is because, PI controller gives a very satisfactory performance with simple implementation and onsite easy tuned parameters (gains). However, one of the drawbacks of using the derivative (D action) in the PID control is the fast reaction to any changes in the system's error, which in turn will produce undesired highly oscillating response. Therefore, in many applications, it is preferred to use a PI controller instead especially in the noisy systems to reduce the response due to noise. Given the knowledge about the battery's SOC, the PI control methodology can be employed to obtain its power. In this work, the PI permits the BS to give its full power in case of the FC's power is low and the battery's SOC is greater than the mean value SOC* and its power is low. While, the FC gives the full load when the SOC of the battery is lower than the mean value. The PI controller transfer function is shown in eqn. (13): Pbatt Ki ¼ Kp þ E S

(13)

where; E ¼ SOC* e SOC

(14)

where Pbatt and Pfc are the battery and FC powers, respectively, while Pload is the load demand. ap denote penalty coefficient. max denote min and max FC DT is the time sampling. Pmin fc and Pfc min max power, respectively. Pbatt and Pbatt denote min and max battery power, respectively. SOCmin and SOCmax are the min and max battery SOC, respectively. m denotes the SOC balance coefficient. In the ECMS, the DC bus voltage is controlled via the battery converters. Therefore, in the optimization process, supercapacitor power is not taken into consideration. Once, the super-capacitor is discharged, it is recharged via the battery. Accordingly, in each load cycle, only the FC and the battery can handle the total energy of the load.

Mine Blast Algorithm (MBA) The observation of mine bomb explosions inspired Sadollah et al. to suggested an optimization approach namely; Mine Blast Algorithm (MBA) [68]. The main target of the technique is to find the mine bomb that has the most explosive effect and causes the greatest number of casualties. The shot point, which is represented by xn0, is the initial mine bomb in the algorithm, where n is the shot points number. These points produce shrapnel pieces which are representing the individuals of number Ns. The new location of the mine bomb can be explored by the shrapnel pieces according to the following formula:  qffiffiffiffiffiffiffiffiffiffi m n xðtþ1Þn ¼ xeðtþ1Þn þ xnt e

Fig. 3 e State machine control.



ðtþ1Þ d ðtþ1Þn

t ¼ 0; 1; 2; …::n

(19)

where xn(tþ1) is the exploded bomb location; xnt is the shot point location; mn(tþ1) the direction of shrapnel pieces and dn(tþ1) is the distance of shrapnel pieces. The next exploited position,

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

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xne(tþ1), is dependent on the shrapnel pieces angle and it is described as follows:   360 n xneðtþ1Þ ¼ dt  rand  cos Ns

(20)

An exploration factor, m, usually predefined by the user, is compared with the current iteration index, k, to decide which action is taken placed; exploration or exploitation. The exploration is performed when m is greater than k. At this process, the new position of the mine bomb is computed according to the following eqn.:   360 n 2 t ¼ 0; 1; 2; ::: xneðtþ1Þ ¼ dt  jrandnj  cos Ns

(21)

The exploitation process is then taken place as long as m is smaller than k. In this stage, the initial distance is reduced gradually to converge to the optimum solution. This is done by using factor a which is defined by user, the reduction is done via eqn. (22). n

d n dt ¼ t1

(22)

k a

e

During exploitation phase, the direction and distance of the shrapnel pieces are computed as in the following eqns.: n

dtþ1 ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2ffi ðxntþ1  xnt Þ2 þ Fntþ1  Fnt

mntþ1 ¼

Fntþ1  Fnt xntþ1  xnt

(23)

(24)

where Fntþ1 is the cost function at location t þ 1. The processes of the MBA are illustrated in a flowchart as shown in Fig. 4. The MBA technique is used to minimize the H2 consumption based on two different energy management strategies. The first one is the ECMS (presented in Equivalent Consumption Minimization Strategy (ECMS)) while the second is the EEMS (presented in External Energy Maximization Strategy (EEMS)).

Salp swarm algorithm (SSA) SSA has been presented by Mirjalili et al. [69]. It has been inspired from the salp's swarming behavior during navigation and foraging in oceans. Salp is a free-swimming marine invertebrate and related to the sea spurts with transparent, barrel-shaped body. At the bottom of the ocean, these creatures gather and form a swarm named salp chain. It is believed that, this swarm is done for obtaining better movement with achieving foraging process. In SSA model, the population individuals are divided into leader (head) salp and the others are the followers, the leader one leads the others in searching the best food. In SSA, the positions of salps in the chain are stored in matrix x of a dimension nagent  dim, where nagent is the number of search agents and dim is the number of decision variables to be optimized. The main target is obtaining the food, F, in the search space, each salp's location is updated based on the information received from the head of the swarm which updates its position via the following formula:

   Fj þ c1 uj  lj c2 þ lj c3  0    xleader a j Fj  c1 uj  lj c2 þ lj c3 30

(25)

where xleader is the position of the leader in dimension j, Fj is j the food position, uj is the upper boundary, and lj is the lower one, c1, c2 and c3 are random values. The value of c1 balances between the exploration and exploitation processes is given by:  c1 ¼ 2  e





4k kmax

(26)

where k and kmax are the current iteration index and the maximum number of iterations, respectively. The update equation of the follower salps' positions are computed as follows: xij ¼

 1
i x þ xi1 i ¼ 2; 3; :::; dim  1 j 2 j

(27)

are the positions of ith salp and the previous where xij and xi1 j one in jth dimension, respectively. Fig. 5 shows flow chart of the main steps of SSA. The algorithm's operation begins by initializing a matrix of salps' positions with the aid of upper and lower bounds of the variables to be designed. The corresponding fitness function for each position is computed and the best one is assigned as the food source. Every salp's position is updated for reaching the recommended food source by the leader. The SSA technique is used to minimize the hydrogen consumption based on two different strategies. The first one is the ECMS which (please refer to Equivalent Consumption Minimization Strategy (ECMS)) and the second is the EEMS (please refer to External Energy Maximization Strategy (EEMS)).

Results and discussions The presented analysis is performed with a load representing more aircraft with electric landing gear and control flight system, while the hybrid system-based FC represents an emergency power supply system. The air driven generator exists in the air craft represents the peak demand seen by the designed hybrid FC system. During landing process, the air driven generator becomes overloaded with zero generated power, so an emergency hybrid source of FC, battery and SC is employed for aircraft safe landing. The presented system comprising FC/battery/supercapacitor is shown in Fig. 6, as shown the FC and SC are linked to the DC bus via DC-DC converter while the battery is connected directly. The generated power is fed to the aircraft via bidirectional inverter. In the presented system, it's preferable to use batteries instead of compressed air storage þ turbine þ generator. The replacement of batteries with air storage, turbine and generator makes the system complicated and unsuitable for the aircraft load. The performance comparison of the studied EMSs is done through MATLAB© simulation. The MATLAB model of the proposed FC/BS/SC hybrid system consists of the following: a 12.5 kW, 30e60 V PEMFC type; a 48 V, 40 Ah battery storage; 6 series-connected SC with rating of 15.6 F, 291.6 V; A 12.5 kW FC DC/DC boost converter. For controlling the battery storage, two DC/DC converters are used. The first one of 4 kW

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

international journal of hydrogen energy xxx (xxxx) xxx

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Fig. 4 e Flowchart of MBA boost type to discharge battery and the second of 1.2 kW buck type to charge it again. A 15 kVA, 200 V AC, 400 Hz inverter is used to feed the AC load demand. For fair assessment of the simulations, the initial conditions for the BS and SC system are set to the same values. The comparison focused hybrid system's hydrogen consumption and the overall efficiency of the system. Table 4 shows the tabulated data of the comparison between different management strategies. The key factors of comparison are H2 consumption, overall efficiency, percentage of decrease in efficiency and percentage of increase in H2 consumption. The efficiency of the approach is calculated by dividing the total demand and the total power delivered from FC, battery and SC. The percentages of increase or decrease are taken relative to the SSA based EEMS as a datum because it has the best efficiency and H2 consumption.

Referring to the obtained results, SSA based EEMS gives the best minimum H2 consumption of values 19.4 gm with efficiency of 85.61% with zero decrement in efficiency and zero increment in H2 consumption as it selected as reference for the two later parameters. The second ranked approach is MBA based ECMS with H2 consumption of 19.85 gm and efficiency of 81.36%. The decrement in efficiency is 2.267% w.r.t the best approach and increment of H2 consumption of 5.22%. The other strategies are ranked as following; MBA based ECMS, PI, EEMS, SMCS, FLC and EECM. Fig. 7 shows the hydrogen consumption and system efficiency for each studied strategy (see Fig. 8). The time responses of hydrogen consumptions resulting from each management strategy are shown in Figs. 9 and 10. The obtained curves confirmed the superiority of SSA based

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

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Fig. 5 e SSA steps.

Fig. 6 e Fuel cell/battery/supercapacitor hybrid aircraft system. Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

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Table 4 e Comparison between different management strategies. EMS PI SMCS ECMS EEMS FLC MBA based ECMS MBA based EEMS SSA based ECMS SSA based EEMS

H2 consumption (gm)

Efficiency (%)

% decrement in efficiency

% increment in H2 consumption

31.6335 32.063 35.9708 31.6774 32.1897 19.85 31.7028 19.9549 19.40

73.77 78.52 72.51 74.15 74.77 81.36 73.815 81.29 85.61

38.6726 39.4941 46.0673 38.7576 39.7322 2.26700 38.8066 2.78077 0.0000

16.0498 9.02954 18.0664 15.4551 14.4977 5.22369 15.9791 5.31430 0.0000

EEMS approach as it achieves the best minimum hydrogen consumptions compared to the others. Figs. 10e12 can help in explaining and hence understanding the EMS behavior of the system's operation during a time span of 350 s. In the following, the critical points of operation are explained in details: when the system starts

i.e. at t ¼ 0, the load has no demand (Pload ¼ 0) so the FC starts in charging the battery. However, after 40 s, the main power supply is disturbed and the designed emergency FC/BS/SC system starts to supply the power to the load and the excess in load demand is handled using the SC since its response to any sudden power demand is fast. At the same time, the FC

Fig. 7 e Hydrogen consumption and system efficiency for each studied strategy.

Fig. 8 e Time-responses of H2 consumption in lpm for the considered EMS strategies. Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

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Fig. 9 e Time-responses of H2 consumption in gm of the considered EMS strategies.

Fig. 10 e Load variations and the generation powers versus time.

power increases gradually. Then, after 5 s later, i.e. at t ¼ 45 s, the SC voltage level begins to decrease under the reference voltage level (270 V). Consequently, the battery is handling the regulation of the SC voltage in order to return back to the reference value. While at t ¼ 48.5 s, the SC voltage is the same as the reference voltage so the BS begins to decrease its power gradually until reaching zero. At this point, the FC supplies the total load demand and recharges the SC also. At t ¼ 60 s, the SC supplies the extra transient load demand, whereas the FC power increases gradually. At t ¼ 61.5 s, the BS regulates the DC bus voltage to the reference level and supports the FC in order to supply the additional demand in the load power. At t ¼ 70 s, the FC goes to its maximum output power and the load's extra demand is handled by the battery. At t ¼ 110 s, the BS also goes to its maximum power and in this case the SC handles the load's excess power. At t ¼ 125 s, the load power is decreasing to a value lower than the FC maximum power. Due to the slow

transient response of the FC, the extra FC energy is then used to charge the supercapacitor. At t ¼ 126 s, the DC bus voltage goes 270 V however the BS is discharging and drops to zero. At t ¼ 170 s, the load demand is decreasing to a value lower than the FC maximum power therefore, the extra FC power is then used to charge both the battery and supercapacitor. At t ¼ 180 s, the load is increased rapidly, thus the SC is responding quickly to this fast change and handles the extra load demand. At t ¼ 185 s, to regulate the DC bus voltage, the BS discharges to help the FC in supplying the power to the extra load demand. At t ¼ 235 s, the load power is abruptly decreased. Accordingly, the exceeded FC energy is then used in charging the BS and SC. At t ¼ 250 s, the FC approximately supplies the total load power. At t ¼ 330 s, the load demand reduces to zero and therefore the FC is decreasing its power gradually to its optimal power and is also recharging the BS. Referring to Fig. 10, the presented SSA based EEMS employed in hybrid system keeps the

Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

international journal of hydrogen energy xxx (xxxx) xxx

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Fig. 11 e The SOC, current and voltage responses of the battery versus time of the considered EMS strategies.

Fig. 12 e The SC's current and voltage responses versus time of the considered EMS strategies. Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195

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power generated from battery at a constant value during a large period of time. This situation is due to the assumption that the employed FC is capable of supplying the load's needs with sharing the power of the battery at a constant value.

Conclusions A comprehensive comparison among nine energy management strategies is done. These strategies include salp swarm algorithm (SSA) and mine-blast optimization (MBO) and other conventional strategies such as fuzzy logic control (FLC); PI classical control; the state machine strategy, equivalent consumption minimization and maximization strategy; external energy maximization strategy (EEMS) and equivalent consumption minimization strategy (ECMS). These strategies are used to obtain the optimal performance addressing the highly fluctuating load demand among different power sources in the hybrid system that include FCs, battery storage and SCs in order to predict the adequate operation parameters. The key factors of comparison are overall efficiency, percentage of decrease in efficiency and percentage of increase in H2 consumption. The percentages of increase or decrease are taken relative to the SSA based EEMS as a datum because it has the best efficiency and H2 consumption. The best obtained minimum consumed hydrogen and the maximum efficiency are 19.4 gm and 85.61%, respectively. The other strategies are ranked in a descending order according their performance as follows; MBA based ECMS, PI, EEMS, SMCS, FLC and finally EECM. In future works, it's recommended to consider the system overall cost in optimization process of hybrid FC/Battery/SC with EMS.

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Please cite this article as: Rezk H et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2019.11.195