{"title":"基于进化模糊系统的混合动力汽车能量管理策略设计第二部分:基于遗传算法的模糊控制器整定","authors":"Aihua Wang, Weizi Yang","doi":"10.1109/WCICA.2006.1713600","DOIUrl":null,"url":null,"abstract":"This paper presents the second part of a two-part paper on development of an evolutionary fuzzy energy management strategy for parallel hybrid vehicles. In this part, we utilized genetic algorithms (GA) to optimize the parameters of the fuzzy controller. In addition, we employed a novel method to cope with the difficulties often encountered in designing a fitness function of GA. The simulation study reveals that the proposed \"evolutionary fuzzy system\" based energy management strategy provide a platform of new energy management system and gives improved performance of a parallel hybrid vehicle","PeriodicalId":375135,"journal":{"name":"2006 6th World Congress on Intelligent Control and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Design of Energy Management Strategy in Hybrid Electric Vehicles by Evolutionary Fuzzy System Part II: Tuning Fuzzy Controller by Genetic Algorithms\",\"authors\":\"Aihua Wang, Weizi Yang\",\"doi\":\"10.1109/WCICA.2006.1713600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the second part of a two-part paper on development of an evolutionary fuzzy energy management strategy for parallel hybrid vehicles. In this part, we utilized genetic algorithms (GA) to optimize the parameters of the fuzzy controller. In addition, we employed a novel method to cope with the difficulties often encountered in designing a fitness function of GA. The simulation study reveals that the proposed \\\"evolutionary fuzzy system\\\" based energy management strategy provide a platform of new energy management system and gives improved performance of a parallel hybrid vehicle\",\"PeriodicalId\":375135,\"journal\":{\"name\":\"2006 6th World Congress on Intelligent Control and Automation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 6th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2006.1713600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 6th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2006.1713600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of Energy Management Strategy in Hybrid Electric Vehicles by Evolutionary Fuzzy System Part II: Tuning Fuzzy Controller by Genetic Algorithms
This paper presents the second part of a two-part paper on development of an evolutionary fuzzy energy management strategy for parallel hybrid vehicles. In this part, we utilized genetic algorithms (GA) to optimize the parameters of the fuzzy controller. In addition, we employed a novel method to cope with the difficulties often encountered in designing a fitness function of GA. The simulation study reveals that the proposed "evolutionary fuzzy system" based energy management strategy provide a platform of new energy management system and gives improved performance of a parallel hybrid vehicle
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