In the last decades, due to emissions reduction policies, research focused on alternative powertrains among which electric vehicles powered by fuel cells are becoming an attractive solution. The main issues of these vehicles are the energy management system and the overall fuel economy. An overview of the existing solutions with respect to their overall efficiency is reported in the paper. On the bases of the literature results the more efficient power train scheme has been selected. The present investigation aims at identifying the best control strategy to power a vehicle with both fuel cell and battery to reduce fuel consumption. The optimization of the control strategy is achieved by using a genetic algorithm. To model the powertrain behaviour an on purpose made simulation program has been developed and implemented in Matlab/Simulink. In particular, the fuel cell model is based on the theory of Amphlett, whereas the battery model also accounts for the charge/discharge efficiency. The analyzed powertrain is equipped with an energy recovery system. During acceleration, power is demanded to the storage system, while during deceleration the battery is recharged. All the tested control strategies assume that the battery SOC can vary between a minimum and a maximum value and that the fuel cell system has to work around its maximum efficiency. All the tested strategies have been validated on the New European Driving Cycle (NEDC) and on the Urban Dynamometer Driving Schedule (UDDS).
Control Strategy Optimization of a Fuel-Cell Electric Vehicle
DE RISI, Arturo;DONATEO, Teresa;LAFORGIA, Domenico
2005-01-01
Abstract
In the last decades, due to emissions reduction policies, research focused on alternative powertrains among which electric vehicles powered by fuel cells are becoming an attractive solution. The main issues of these vehicles are the energy management system and the overall fuel economy. An overview of the existing solutions with respect to their overall efficiency is reported in the paper. On the bases of the literature results the more efficient power train scheme has been selected. The present investigation aims at identifying the best control strategy to power a vehicle with both fuel cell and battery to reduce fuel consumption. The optimization of the control strategy is achieved by using a genetic algorithm. To model the powertrain behaviour an on purpose made simulation program has been developed and implemented in Matlab/Simulink. In particular, the fuel cell model is based on the theory of Amphlett, whereas the battery model also accounts for the charge/discharge efficiency. The analyzed powertrain is equipped with an energy recovery system. During acceleration, power is demanded to the storage system, while during deceleration the battery is recharged. All the tested control strategies assume that the battery SOC can vary between a minimum and a maximum value and that the fuel cell system has to work around its maximum efficiency. All the tested strategies have been validated on the New European Driving Cycle (NEDC) and on the Urban Dynamometer Driving Schedule (UDDS).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
