Hu Jianyao, Xu Huawei, H. Zhiyuan, Huang Linyi, Liu Qunxing
{"title":"基于模糊控制算法的制动力分配研究","authors":"Hu Jianyao, Xu Huawei, H. Zhiyuan, Huang Linyi, Liu Qunxing","doi":"10.1109/IAEAC.2015.7428732","DOIUrl":null,"url":null,"abstract":"One of the advantages of electric vehicle is its ability to regenerate braking energy compared with conventional motor vehicles. This is able to save the electric energy and extend the drive range of electric vehicle. However, on one hand, regeneration and usage of braking energy with a higher efficiency is still a topic to be studied. On the other hand, allocation of the general braking force between the normal mechanical and electric braking systems in EV seriously affects its safety. In this paper, the braking force distribution during regenerative braking processes of a battery electric bus is studied. Controlled by a fuzzy logic strategy, the regenerative braking energy model is developed, which is embedded in the entire vehicle model. With the theoretical model, the braking force distribution function has been calculated with two factors, vehicle velocity and battery state of charge (SoC). The strategy also takes the comfort requirements of the driver during braking into account. The simulation results in this paper show that in addition to properly distributing the braking force, the regenerative braking energy control strategy can save the energy consumption of the electric bus by 5.5%, 3.9% and 6.4% during China city bus driving cycle, New York bus driving cycle and JapeneselO-15 mode driving cycle, respectively.","PeriodicalId":398100,"journal":{"name":"2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Study on braking force distribution based on fuzzy control algorithm\",\"authors\":\"Hu Jianyao, Xu Huawei, H. Zhiyuan, Huang Linyi, Liu Qunxing\",\"doi\":\"10.1109/IAEAC.2015.7428732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the advantages of electric vehicle is its ability to regenerate braking energy compared with conventional motor vehicles. This is able to save the electric energy and extend the drive range of electric vehicle. However, on one hand, regeneration and usage of braking energy with a higher efficiency is still a topic to be studied. On the other hand, allocation of the general braking force between the normal mechanical and electric braking systems in EV seriously affects its safety. In this paper, the braking force distribution during regenerative braking processes of a battery electric bus is studied. Controlled by a fuzzy logic strategy, the regenerative braking energy model is developed, which is embedded in the entire vehicle model. With the theoretical model, the braking force distribution function has been calculated with two factors, vehicle velocity and battery state of charge (SoC). The strategy also takes the comfort requirements of the driver during braking into account. The simulation results in this paper show that in addition to properly distributing the braking force, the regenerative braking energy control strategy can save the energy consumption of the electric bus by 5.5%, 3.9% and 6.4% during China city bus driving cycle, New York bus driving cycle and JapeneselO-15 mode driving cycle, respectively.\",\"PeriodicalId\":398100,\"journal\":{\"name\":\"2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAEAC.2015.7428732\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC.2015.7428732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on braking force distribution based on fuzzy control algorithm
One of the advantages of electric vehicle is its ability to regenerate braking energy compared with conventional motor vehicles. This is able to save the electric energy and extend the drive range of electric vehicle. However, on one hand, regeneration and usage of braking energy with a higher efficiency is still a topic to be studied. On the other hand, allocation of the general braking force between the normal mechanical and electric braking systems in EV seriously affects its safety. In this paper, the braking force distribution during regenerative braking processes of a battery electric bus is studied. Controlled by a fuzzy logic strategy, the regenerative braking energy model is developed, which is embedded in the entire vehicle model. With the theoretical model, the braking force distribution function has been calculated with two factors, vehicle velocity and battery state of charge (SoC). The strategy also takes the comfort requirements of the driver during braking into account. The simulation results in this paper show that in addition to properly distributing the braking force, the regenerative braking energy control strategy can save the energy consumption of the electric bus by 5.5%, 3.9% and 6.4% during China city bus driving cycle, New York bus driving cycle and JapeneselO-15 mode driving cycle, respectively.