ABSTRACT:
This
paper presents a comparative analysis of different energy management schemes
for a fuel-cell-based emergency power system of a more-electric aircraft. The
fuel-cell hybrid system considered in this paper consists of fuel cells,
lithium-ion batteries, and supercapacitors, along with associated dc/dc and dc/ac
converters. The energy management schemes addressed are state of the art and
are most commonly used energy management techniques in fuel-cell vehicle
applications, and they include the following: the state machine control
strategy, the rule-based fuzzy logic strategy, the classical
proportional–integral control strategy, the frequency decoupling/fuzzy logic
control strategy, and the equivalent consumption minimization strategy. The
main criteria for performance comparison are the hydrogen consumption, the state
of charges of the batteries/supercapacitors, and the overall system efficiency.
Moreover, the stresses on each energy source, which impact their life cycle,
are measured using a new approach based on the wavelet transform of their instantaneous
power. A simulation model and an experimental test bench are developed to validate
all analysis and performances.
KEYWORDS:
1.
Batteries
2.
Dc–dc converters
3.
Energy management
4.
Fuel cells
5.
Hybridization
6.
Optimization
7.
Supercapacitors
SOFTWARE: MATLAB/SIMULINK
BLOCK DIAGRAM:
Fig. 1 Proposed system
block diagram using AFLC
Fig. 2. DC/DC converter model
validation. (a) Fuel-cell boost converter. (b) Battery boost converter. (c)
Battery buck converter.
Fig.
3 DC/AC converter model validation.
Fig.
4. Simulation and experimental results for all EMS schemes. (a) Simulation
results for state machine control. (b) Experimental results for state machine
control.
(c) Simulation results for rule-based fuzzy logic. (d) Experimental results for
rule-based fuzzy logic. (e) Simulation results for classical PI control.
(f)
Experimental results for classical PI control. (g) Simulation results for
frequency decoupling and fuzzy logic. (h) Experimental results for frequency decoupling
and
fuzzy logic. (i) Simulation results for ECMS. (j) Experimental results for
ECMS.
CONCLUSION:
This paper has presented a
performance comparison of different energy management schemes for a fuel-cell
hybrid emergency system of MEA. The hybrid system is modeled and validated with experiments. Five
state-of-the-art commonly used energy management schemes are studied through
simulations and experimental tests on a 14-kW fuel-cell hybrid system. The same initial condition is used
for all the schemes, and the experimental results are close to simulations. The
criteria for performance comparison are the hydrogen consumption, the battery
SOC, the overall efficiency, and the stress seen by each energy source. The latter
is measured using a new approach based on wavelet transform. Compared with the
other schemes, the state machine control scheme provided slightly better
efficiency (80.72%) and stresses on the battery
and supercapacitor (σ of 21.91 and 34.7, respectively). The classical
PI control scheme had the lowest fuel consumption (235 g of H2 consumed)
and more use of the battery energy (SOC between 70%–51%). As expected, the
lowest fuel-cell stress (σ of 12.04) and the lowest use of the battery
energy (SOC between 70%–59%) were achieved with the frequency decoupling and
fuzzy logic scheme but at the expense of more fuel consumption (245 g of H2
consumed) and lower overall efficiency (79.32%). The dc-bus or supercapacitor
voltage was maintained nearly constant (≈270 Vdc) for all the schemes. To
conclude, the energy management system suitable for MEA should be a multischeme
EMS, such that each scheme is chosen based on a specific criterion to
prioritize. As an example, depending on the operating life of each energy source,
the EMS can be chosen to either minimize the stress on the fuelcell system, the
battery system, or the supercapacitor system, hence maximizing the life cycle
of the hybrid power system. In addition, if the target is to reduce the fuel
consumption, the strategy based on the classical PI or ECMS could be selected. An
alternative is to design a multiobjective optimization EMS to optimize all the performance criteria,
which is the next topic for further studies.
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