Genetic Algorithm optimization of Fuel Cell/Battery propulsion system components

Abstract

The objective of this thesis is the development of a methodology to determine the sizing of powertrain components in a Fuel Cell/Battery vehicle. The analysis will involve the study of the best powertrain configuration to optimize costs and performances in different missions and scenarios. For this purpose, an optimization procedure called Genetic Algorithm, able to autonomously find the best compromise will be adopted. The GA procedure is coupled with a vehicle model in which Fuel Cell system model has been improved using experimental data to better represent the dynamic behavior of a real Fuel Cell. The study on the Fuel Cell will be focused on the influence of temperature and reagents supply power generation. In conclusion, an applicative case will be investigated based on a generic mid-size Light Commercial Vehicle and economic analysis on the different configurations is given to identify a cost-effective choice among them.

Obiettivo Tesi

Modeling of the fuel cell system, application of genetic algorithms to optimize the size of the components and the overall cost from a customer and OEM needs perspective.

Metodologia di ricerca

Use of existing models, improvement of fuel cell modeling, the definition of the cost-objective function, use of genetic optimization algorithms. Tools used: Matlab, Simulink, Stateflow.