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 study 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, most commonly used energy management techniques in fuel cell vehicle applications and include: the state machine control strategy, the rule based fuzzy logic strategy, the classical PI control strategy, the frequency decoupling/fuzzy logic control strategy and the equivalent consumption minimization strategy (ECMS). 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.
- Fuel cells
- DC-DC converters
- Energy management
Fig. 1. Overall system schematic
EXPECTED SIMULATION RESULTS:
Fig. 2. Simulation and experimental results for all EMS schemes: (a) Simulation results. State machine control (b) Experimental results. State machine control (c) Simulation results. Rule based fuzzy logic (d) Experimental results. Rule based fuzzy logic (e) Simulation results. Classical PI control (f) Experimental results. Classical PI control (g) Simulation results. Frequency decoupling and fuzzy logic (h) Experimental results. Frequency decoupling and fuzzy logic (i) Simulation results. ECMS (j) Experimental results. ECMS
This paper presented a performance comparison of different energy management schemes for a fuel cell hybrid emergency system of more electric aircraft. The hybrid system is modelled 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 state of charge, the overall efficiency and the stress seen by each energy source. The latter is measured using a new approach based on wavelet transform. Compared to the other schemes, the state machine control scheme provided a slightly better efficiency (80.72%) and stresses on the battery and supercapacitor (_ 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 (_ of 12.04) and lowest use of the battery energy (SOC between 70 – 59 %) was 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 V DC) for all the schemes. To conclude, the energy management system suitable for MEA should be a multi-scheme 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 energy management strategy can be chosen to either minimize the stress on the fuel cell system, the battery system or supercapacitor system, hence maximizing the life cycle of the hybrid power system. Also 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 multi-objective optimization EMS to optimize all the performance criteria, which is the next topic for further studies.
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