Optimal design of a parallel Hybrid Electric Vehicle using multi-objective genetic algorithms

Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, optimal powertrain component sizing is inevitable. This pape...

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Bibliographic Details
Published in:2009 IEEE Vehicle Power and Propulsion Conference pp. 871 - 876
Main Authors: Desai, C., Williamson, S.S.
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2009
Subjects:
Algorithm design and analysis
Constraint optimization
Control
Design optimization
electric propulsion
electric vehicles
energy storage
Fuel economy
Genetic algorithms
Hybrid electric vehicles
Ice
Mechanical power transmission
modeling
Optimization methods
Power electronics
road vehicles
traction motor drives
ISBN:9781424426003, 1424426006
ISSN:1938-8756
Online Access:Get full text
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Summary:Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, optimal powertrain component sizing is inevitable. This paper presents an efficient multi-objective genetic algorithm (MOGA), to optimize powertrain component sizes as well as fuel economy and emissions, including HC, CO, and NOx, for a parallel HEV. The main target is to find the trade-off solutions, known as pareto-optimal set, from among the objectives. Simulation results show the potential of the proposed optimization technique in terms of improved fuel economy and low emissions.
ISBN:9781424426003
1424426006
ISSN:1938-8756
DOI:10.1109/VPPC.2009.5289754