Concurrent Optimization for Parameters of Powertrain and Control System of Hybrid Electric Vehicle Based on Multi-Objective Genetic Algorithms

The optimizing design of hybrid electric vehicle (HEV) aims at improving fuel economy and decreasing emissions subject to the satisfaction of its drivability. The concurrent optimization for main parameters of powertrain components and control system is the key to implement this objective. However,...

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Published in:2006 SICE-ICASE international joint conference : Busan, Korea, 18-21 October 2006 pp. 2424 - 2429
Main Authors: Li-Cun Fang, Shi-Yin Qin
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2006
Subjects:
Automatic control
Concurrent Optimization
Constraint optimization
Control systems
Design automation
Design optimization
Fuel economy
Genetic algorithms
Hybrid electric vehicle(HEV)
Hybrid electric vehicles
Mechanical power transmission
Multi-Objective genetic algorithms(MOGAs)
Optimization methods
ISBN:9788995003848, 8995003847
Online Access:Get full text
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Abstract The optimizing design of hybrid electric vehicle (HEV) aims at improving fuel economy and decreasing emissions subject to the satisfaction of its drivability. The concurrent optimization for main parameters of powertrain components and control system is the key to implement this objective. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Thus, it is necessary to employ an effective strategy and algorithms to solve this problem. In this paper, an approach of optimization is developed based on the multi-objective genetic algorithms, which can realize the optimization to parameters of powertrain and control system simultaneously and find the Pareto-optimal solution set successfully subject to user-selectable performance constraints. This optimal parameter set provides a wide range of choices for the design, which can improve the fuel economy and reduce emissions without sacrificing vehicle performance. A case simulation is carried out and simulated by ADVISOR, the results demonstrate the effectiveness of the approach proposed in this paper
AbstractList The optimizing design of hybrid electric vehicle (HEV) aims at improving fuel economy and decreasing emissions subject to the satisfaction of its drivability. The concurrent optimization for main parameters of powertrain components and control system is the key to implement this objective. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Thus, it is necessary to employ an effective strategy and algorithms to solve this problem. In this paper, an approach of optimization is developed based on the multi-objective genetic algorithms, which can realize the optimization to parameters of powertrain and control system simultaneously and find the Pareto-optimal solution set successfully subject to user-selectable performance constraints. This optimal parameter set provides a wide range of choices for the design, which can improve the fuel economy and reduce emissions without sacrificing vehicle performance. A case simulation is carried out and simulated by ADVISOR, the results demonstrate the effectiveness of the approach proposed in this paper
Author Shi-Yin Qin
Li-Cun Fang
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  fullname: Shi-Yin Qin
  organization: Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing
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Snippet The optimizing design of hybrid electric vehicle (HEV) aims at improving fuel economy and decreasing emissions subject to the satisfaction of its drivability....
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SubjectTerms Automatic control
Concurrent Optimization
Constraint optimization
Control systems
Design automation
Design optimization
Fuel economy
Genetic algorithms
Hybrid electric vehicle(HEV)
Hybrid electric vehicles
Mechanical power transmission
Multi-Objective genetic algorithms(MOGAs)
Optimization methods
Title Concurrent Optimization for Parameters of Powertrain and Control System of Hybrid Electric Vehicle Based on Multi-Objective Genetic Algorithms
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