Donghui Lv, Lin Yuan, Bo Zhu, Zhidong Liu, Xue Bai
{"title":"考虑平顺性和经济性的电动汽车AMT多目标智能换挡方案研究","authors":"Donghui Lv, Lin Yuan, Bo Zhu, Zhidong Liu, Xue Bai","doi":"10.1504/ijvd.2023.134737","DOIUrl":null,"url":null,"abstract":"Considering the current shifting strategy of multi-speed automatic manual transmission (AMT) separates the steady-state shifting from the transient shifting process in the pure electric vehicle, it is difficult to find a comprehensive improvement of shifting quality, dynamic performance, and driving economy. In this paper, taking advantage of the artificial intelligence technology, a fuzzy neural network (FNN) based T-S model is established via obtaining the training data from skilled drivers' experience and expert knowledge. A two-speed AMT pure electric vehicle model is used to investigate the fuzzy shifting strategy performance. According to the co-simulation results of AMESim and SIMULINK, the average jerk of 10.006 is recorded, compared to the value of 16.472 based on an ordinary shifting schedule. The results show that FNN-based schedule fully reflects drivers' shifting intentions in pursuing shifting smoothness, at the same time, improving vehicle dynamic performance with negligible economic performance loss.","PeriodicalId":54938,"journal":{"name":"International Journal of Vehicle Design","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on multi objective intelligent shifting schedule of electric vehicle AMT considering ride comfort and economy\",\"authors\":\"Donghui Lv, Lin Yuan, Bo Zhu, Zhidong Liu, Xue Bai\",\"doi\":\"10.1504/ijvd.2023.134737\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering the current shifting strategy of multi-speed automatic manual transmission (AMT) separates the steady-state shifting from the transient shifting process in the pure electric vehicle, it is difficult to find a comprehensive improvement of shifting quality, dynamic performance, and driving economy. In this paper, taking advantage of the artificial intelligence technology, a fuzzy neural network (FNN) based T-S model is established via obtaining the training data from skilled drivers' experience and expert knowledge. A two-speed AMT pure electric vehicle model is used to investigate the fuzzy shifting strategy performance. According to the co-simulation results of AMESim and SIMULINK, the average jerk of 10.006 is recorded, compared to the value of 16.472 based on an ordinary shifting schedule. The results show that FNN-based schedule fully reflects drivers' shifting intentions in pursuing shifting smoothness, at the same time, improving vehicle dynamic performance with negligible economic performance loss.\",\"PeriodicalId\":54938,\"journal\":{\"name\":\"International Journal of Vehicle Design\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Vehicle Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijvd.2023.134737\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Vehicle Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijvd.2023.134737","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Research on multi objective intelligent shifting schedule of electric vehicle AMT considering ride comfort and economy
Considering the current shifting strategy of multi-speed automatic manual transmission (AMT) separates the steady-state shifting from the transient shifting process in the pure electric vehicle, it is difficult to find a comprehensive improvement of shifting quality, dynamic performance, and driving economy. In this paper, taking advantage of the artificial intelligence technology, a fuzzy neural network (FNN) based T-S model is established via obtaining the training data from skilled drivers' experience and expert knowledge. A two-speed AMT pure electric vehicle model is used to investigate the fuzzy shifting strategy performance. According to the co-simulation results of AMESim and SIMULINK, the average jerk of 10.006 is recorded, compared to the value of 16.472 based on an ordinary shifting schedule. The results show that FNN-based schedule fully reflects drivers' shifting intentions in pursuing shifting smoothness, at the same time, improving vehicle dynamic performance with negligible economic performance loss.
期刊介绍:
IJVD, the journal of vehicle engineering, automotive technology and components, has been established for over a quarter of a century as an international authoritative reference in the field. It publishes the Proceedings of the International Association for Vehicle Design, which is an independent, non-profit-making learned society that exists to develop, promote and coordinate the science and practice of vehicle design and safety.
Topics covered include
Vehicle engineering design
Automotive technology
R&D of all types of self-propelled vehicles
R&D of vehicle components
Interface between aesthetics and engineering
Integration of vehicle and components design into the development of complete vehicle systems
Social and environmental impacts of vehicle design
Energy
Safety.