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

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 subjec...

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Veröffentlicht in:International journal of automotive technology Jg. 15; H. 4; S. 645 - 654
Hauptverfasser: Geng, B., Mills, J. K., Sun, D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Heidelberg The Korean Society of Automotive Engineers 01.06.2014
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Automobiles
Automotive Engineering
Constraints
Control systems
Design optimization
Efficiency
Electric power generation
Electric vehicles
Energy consumption
Energy management
Energy storage
Engineering
Fuel cell vehicles
Fuel cells
Hydrogen
Investigations
Matlab
Optimization
Optimization algorithms
Ordinary differential equations
Pareto optimality
Pareto optimum
Power management
Powertrain
Studies
Multi-objective optimization
Power management
Particle swarm optimization
Component sizing
Fuel cell
Plug-in hybrid electric vehicle
ISSN:1229-9138, 1976-3832
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Zusammenfassung: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.
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ISSN:1229-9138
1976-3832
DOI:10.1007/s12239-014-0067-x