Multi-objective optimization of hybrid electrical vehicle based on pareto optimality

被引：0
|
作者：
Yang, Guan-Ci [1 ,2 ]
Li, Shao-Bo [1 ,2 ]
Qu, Jing-Lei [1 ]
Gou, Guan-Qi [3 ]
Zhong, Yong [2 ]
机构：
[1] Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550003, China
[2] Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, China
[3] College of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2012年 / 46卷 / 08期
关键词：
Constrained multi-objective optimizations - Emission performance - Hybrid electrical vehicle - Multi objective evolutionary algorithms - Pareto dominance - Pareto optimal solutions - Pareto-optimality - Significant digits;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Some basic theories about hybrid electric vehicle (HEV) were introduced. The multi-objective optimal model for minimizing the fuel consumption, the sum emission of HC and NOx, and the CO emission was established. A multi-objective evolutionary algorithm for hybrid electrical vehicle based on Pareto optimality (HEV-MOEA) was proposed. HEV-MOEA uses real coding method to represent gene, and employs ADVISOR to simulate HEV to obtain the value of each objective of candidate solutions, and adopts the Pareto dominance principle to evaluate each solution. A method was put forward to specify the significant digits of variables to guarantee the realizability. A series of simulation results show that HEV-MOEA is capable of solving the multi-objective optimization design of HEV, and is promising to provide a set of alternative Pareto optimal solutions characterized with better fuel economy and emission performance for designer.
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页码：1297 / 1303
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