Title :
An Econiche Genetic Algorism-Based Optimization of HEV Parameters
Author :
Deng, Yadong ; Lin, Xiang ; Lian, Zhiwei
Author_Institution :
Sch. of Automobile Eng., Wuhan Univ. of Technol., Wuhan, China
Abstract :
A synchronous optimization of power system and controller parameter could substantially improve the fuel economy and emission performance of HEV (hybrid electric vehicle). As a typical multi-objective optimization, this type of optimization problem involves a couple of mutually conflicting objectives of optimization and non-linear constraints. In this paper, the Pareto optimal solution set of this optimization problem is sought by adopting econiche genetic algorithm on a specimen vehicle. The result turns out that several groups of Pareto optimal solutions could be obtained in this way, while the fuel economy and emission performance could be remarkably enhanced with vehicle´s given performance being guaranteed.
Keywords :
Pareto optimisation; fuel economy; genetic algorithms; hybrid electric vehicles; HEV parameters; econiche genetic algorithm based optimization; emission performance; fuel economy; hybrid electric vehicle; multiobjective optimization; mutually conflicting objectives; nonlinear constraints; pareto optimal solution; specimen vehicle; Acceleration; Battery powered vehicles; Constraint optimization; Control systems; Design optimization; Engines; Genetic algorithms; Hybrid electric vehicles; Pareto optimization; Power systems; Pareto optimal solution; econiche genetic algorithm; multi-objective optimization;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.112