Zelong Zhang, Y. Huangfu, Liangcai Xu, Jun Zhao, Wenzhuo Shi, Tianying Yu
{"title":"Research on Optimized Energy Management Strategy Based on Micro-trip Recognition","authors":"Zelong Zhang, Y. Huangfu, Liangcai Xu, Jun Zhao, Wenzhuo Shi, Tianying Yu","doi":"10.1109/peas53589.2021.9628812","DOIUrl":null,"url":null,"abstract":"The energy management strategy of fuel cell electric vehicle can greatly influence the performance of vehicle, so a lot of research in this field were done by researchers. In order to improve the adaptability to complex work conditions of traditional strategies, the work condition recognition methods based on intelligence algorithms were introduced to energy management strategies. However, there are a lot of disadvantages of present recognition methods, such as low recognition accuracy and low generality. Aiming at solving these problems, an optimized fuzzy energy management strategy based on micro-trip recognition is proposed in this paper. In this strategy, firstly the work conditions prepared for recognition are divided into several micro-trips to improve the accuracy of recognition. Then, the strategy is simulated on the simplified power system built in this paper and compared with the simulation result of a general rule-based strategy. The better performance of the strategy proposed proves the effectiveness and optimality of this method.","PeriodicalId":268264,"journal":{"name":"2021 IEEE 1st International Power Electronics and Application Symposium (PEAS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 1st International Power Electronics and Application Symposium (PEAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/peas53589.2021.9628812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The energy management strategy of fuel cell electric vehicle can greatly influence the performance of vehicle, so a lot of research in this field were done by researchers. In order to improve the adaptability to complex work conditions of traditional strategies, the work condition recognition methods based on intelligence algorithms were introduced to energy management strategies. However, there are a lot of disadvantages of present recognition methods, such as low recognition accuracy and low generality. Aiming at solving these problems, an optimized fuzzy energy management strategy based on micro-trip recognition is proposed in this paper. In this strategy, firstly the work conditions prepared for recognition are divided into several micro-trips to improve the accuracy of recognition. Then, the strategy is simulated on the simplified power system built in this paper and compared with the simulation result of a general rule-based strategy. The better performance of the strategy proposed proves the effectiveness and optimality of this method.