Abstract In this paper, the combined power management/design optimization problem is investigated for a fuel cell/Liion battery PHEV. Formulated as a constrained multi-objective optimization problem (MOP), the combined optimization problem simultaneously minimizes the vehicle cost and fuel consumption subject to the vehicle performance requirements. With an emphasis on developing a generic optimization algorithm to find the Pareto front for the synthesized MOP, the Pareto based multi-objective particle swarm optimization (PMOPSO) algorithm is developed, which solely depends on the concept of Pareto dominance. Three approaches are introduced to the PMOPSO method to address the constrained MOP. They are: (i) by incorporating system constraints in the original objective functions, the constrained MOP is transformed to an unconstrained MOP; (ii) to avoid being trapped in local minima, a disturbance operator with a decaying mutation possibility is introduced; (iii) to generate a sparsely distributed Pareto front, the concept of crowding distance is utilized to determine the global guidance for the particles. Finally, under the Matlab/Simulink software environment, simulation results are presented to demonstrate the effectiveness of the PMOPSO in the derivation of the true Pareto front.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Combined power management/design optimization for a fuel cell/battery plug-in hybrid electric vehicle using multi-objective particle swarm optimization


    Contributors:
    Geng, B. (author) / Mills, J. K. (author) / Sun, D. (author)

    Published in:

    Publication date :

    2014-05-28


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English