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Abstract:In this paper, an experimental fuel cell/battery/supercapacitor hybrid system is investigated in terms of modeling and power management design and optimization. The power management strategy is designed based on the role that should be played by each component of the hybrid power source. The supercapacitor is responsible for the peak power demands. The battery assists the supercapacitor in fulfilling the transient power demand by controlling its state-of-energy, whereas the fuel cell system, with its slow dynamics, controls the state-of-charge of the battery. The parameters of the power management strategy are optimized by a genetic algorithm and Pareto front analysis in a framework of multi-objective optimization, taking into account the hydrogen consumption, the battery loading and the acceleration performance. The optimization results are validated on a test bench composed of a fuel cell system (1.2 kW, 26 V), lithium polymer battery (30 Ah, 37 V), and a supercapacitor (167 F, 48 V).

In this paper, the optimization of power management strategy of a fuel cell/battery/supercapacitor hybrid vehicular system is investigated, both off-line and in real-time. Two off-line optimization algorithms, Dynamic Programming and Pontryagin's Minimum Principle, are first compared. The off-line optimum is used then as a benchmark when designing a real-time strategy, which is an inevitable step since the off-line optimum is not real-time capable and is oriented only towards minimizing the hydrogen consumption, which may result in unnecessary overloading of the battery. The design and optimization of the real-time strategy makes use of a multiobjective genetic algorithm while taking into account, besides hydrogen consumption, other important factors such as the slow dynamics of the fuel cell system and minimizing the battery power burden. As a result, the real-time strategy is found to consume slightly more hydrogen than the off-line optimum; however, it dramatically improves the system durability.

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