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Communication Dans Un Congrès Année : 2014

Optimal frequency separation of power sources by multivariable LPV/Hinf control: application to on-board energy management systems of electric vehicles

Résumé

In this paper a multi-variable LPV/Hinf control approach is applied to design a strategy for power source coordination within a multi-source energy system. Three different kinds of power sources - fuel cell, battery and ultracapacitor - compose the power supply system of an electric vehicle. All sources are current-controlled and paralleled together with their associated DC-DC converters on a common DClink coupled to vehicle's electrical motor and its converter. DC-link voltage must be regulated in spite of load power variations representing the driving cycle image. To this end, a MIMO LPV/Hinf provides the three current references so that each source operates in its most suitable frequency range as either high-energy-density or high-power-density source: lowfrequency, mean power is provided by fuel cell, ultracapacitor supplies/absorbs the instantaneous variations of power demand and battery operates in between the two other sources. Selection of Hinf weighting functions is guided by a genetic algorithm whose optimization criterion expresses the frequency separation requirements. The nonlinear multi-source system is simulated in MATLAB®/Simulink® using the driving cycle of IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux) as load profile, whose frequency content is richer than that of Normalized European Driving Cycle (NEDC). Simulation results show good performance in supplying the load at constant DC-link voltage according to user-configured frequency-separation power sharing strategy.
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Dates et versions

hal-01037855 , version 1 (02-08-2016)

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  • HAL Id : hal-01037855 , version 1

Citer

Waleed Nwesaty, Antoneta Iuliana Bratcu, Olivier Sename. Optimal frequency separation of power sources by multivariable LPV/Hinf control: application to on-board energy management systems of electric vehicles. CDC 2014 - 53rd IEEE Conference on Decision and Control, Dec 2014, Los Angeles, United States. pp.5636-5641. ⟨hal-01037855⟩
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