Multiobjective Optimization of HEV Fuel Economy and Emissions Using the Self-Adaptive Differential Evolution Algorithm

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal...

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Vydáno v:IEEE transactions on vehicular technology Ročník 60; číslo 6; s. 2458 - 2470
Hlavní autoři: Wu, Lianghong, Wang, Yaonan, Yuan, Xiaofang, Chen, Zhenlong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.07.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Applied sciences
Batteries
Differential evolution (DE) algorithm
Driving
Electrical engineering. Electrical power engineering
Electrical machines
emissions
Engines
Evolution
Exact sciences and technology
Fuel consumption
fuel consumption (FC)
Fuels
Ground, air and sea transportation, marine construction
Hybrid electric vehicles
hybrid electric vehicles (HEVs)
Hybrid vehicles
multiobjective optimization
Optimization
Pareto optimality
Regulation and control
Road transportation and traffic
Strategy
Studies
Torque
Hybrid electric powered vehicles
Archive
Adaptive algorithm
Evolutionary algorithm
Differential evolution (DE) algorithm
Evolutionary computation
Multiobjective programming
Fuel consumption
Optimization
Optimal design
multiobjective optimization
Performance requirement
Fuel economy
Fuzzy set theory
emissions
hybrid electric vehicles (HEVs)
Nearest neighbour
Pareto optimum
Control system
Nonlinear problems
Electric control
Dimensioning
European community
Optimal solution
fuel consumption (FC)
Comparative study
ISSN:0018-9545, 1939-9359
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Shrnutí:This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE+EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2011.2157186