Third International Conference on Ecological Vehicles & Renewable Energies March 27 - 30
A Study on the Optimization of the Design and Control Parameters of a Fuel-Cell Hybrid Electric Vehicle with MultiObjective Genetic Algorithms Teresa Donateo, Arturo de Risi and Domenico Laforgia Università del Salento, Dipartimento di Ingegneria dell’Innovazione, Via Monteroni 73100 Lecce, Italy E-mail:
[email protected] Copyright © 2008 MC2D & MITI
Abstract: The aim of the present investigation is to analyze the performance of multi-objective genetic algorithms in the optimization of a Hybrid Electric powertrain powered by a PEM fuel cell with both batteries and supercapacitors as secondary energy storage systems. To model the powertrain, an on-purpose simulation program (ECoS) implemented in Matlab/Simulink environment, has been used. The fuel cell model is based on the Amphlett theory, whereas the battery and super-capacitor models also take into account the charge/discharge efficiency. The analyzed powertrain is also equipped with an energy regeneration system to store braking energy. Several quantitative metrics were used to evaluate the behavior of the genetic algorithm when changing its parameters (number of generation, probability of crossover, probability of mutation, selection of the Pareto solutions, etc.) Keywords: Hydrogen, PEM fuel cell, Hybrid Electric Vehicle, Multi-objective optimization 1. Introduction In the last decades, due to emissions reduction policies, research focused on alternative powertrains among which hybrid electric vehicles (HEVs) powered by fuel cells are becoming an attractive solution. One of the main issues of these vehicles is the energy management (also named supervisory control) that has to be accurately designed in order to improve the overall fuel economy. However, in the design of a hybrid electric vehicle, the hardware parameters (number and size of the components) must be optimized together with the control parameters that govern the energy flows, in order to fully exploit the potential of hybrid architectures. Furthermore, multiple and competitive goals must be considered. The evaluation of a vehicle configuration, particularly when based on fuel economy metrics, requires appropriate driving and duty cycles according to the vehicle intended use. Constraints, restrictions and limits that the designer must meet due to
norms, regulations and functionalities are additional challenges in the design process. When several outputs need to be optimized at the same time and the achievement of a goal implies the penalization of others, the optimization problem is named multi-objective and its solution is usually very complex. In fact, for this kind of problems the notion of best solution is meaningless and a compromise must be found in the optimization of the objectives. In single-objective optimization problems, the capability of a solution to achieve the goal is called fitness. In the case of competitive goals an array of fitness components can be assigned to each solution, whose components are the fitness values calculated separately according to each objective. Each couple of solutions is compared using the Pareto criterion, based on dominance concept described as follows. In a maximization problem, a fitness vector x is said to be dominated or partially less (
