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

• Published in: IEEE transactions on vehicular technology Vol. 60; no. 6; pp. 2458 - 2470
• Main Authors: Wu, Lianghong, Wang, Yaonan, Yuan, Xiaofang, Chen, Zhenlong
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TVT.2011.2157186

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.