{"title":"基于模拟退火算法优化的城市标准行驶工况构建","authors":"Hang Zhang, Siwen Lv, Yu Zhang, S. Zhang","doi":"10.1109/CVCI51460.2020.9338485","DOIUrl":null,"url":null,"abstract":"In order to assess the vehicle emissions and energy consumption in actual driving, the accurate vehicle driving cycles are extremely necessary. On the basis of the previous driving cycle's construction methods, the innovation of this paper is proposing a method for constructing urban driving cycle based on simulated annealing algorithm. The major task is the data processing and optimizing. For data processing, the characteristic parameter of the micro-trips is selected according to the theory of micro-trips analysis, then this paper performs principal component analysis to reduce the dimensions of motion characteristic parameters and the K-means clustering method is used to classify kinematics segments. In the selection of fragments, this paper adopts the simulated annealing algorithm to optimize. The final analysis results show that the error is largely reduced and the accuracy of the operating conditions is further improved.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Construction of urban standard driving cycle based on simulated annealing algorithm optimization\",\"authors\":\"Hang Zhang, Siwen Lv, Yu Zhang, S. Zhang\",\"doi\":\"10.1109/CVCI51460.2020.9338485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to assess the vehicle emissions and energy consumption in actual driving, the accurate vehicle driving cycles are extremely necessary. On the basis of the previous driving cycle's construction methods, the innovation of this paper is proposing a method for constructing urban driving cycle based on simulated annealing algorithm. The major task is the data processing and optimizing. For data processing, the characteristic parameter of the micro-trips is selected according to the theory of micro-trips analysis, then this paper performs principal component analysis to reduce the dimensions of motion characteristic parameters and the K-means clustering method is used to classify kinematics segments. In the selection of fragments, this paper adopts the simulated annealing algorithm to optimize. The final analysis results show that the error is largely reduced and the accuracy of the operating conditions is further improved.\",\"PeriodicalId\":119721,\"journal\":{\"name\":\"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVCI51460.2020.9338485\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVCI51460.2020.9338485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Construction of urban standard driving cycle based on simulated annealing algorithm optimization
In order to assess the vehicle emissions and energy consumption in actual driving, the accurate vehicle driving cycles are extremely necessary. On the basis of the previous driving cycle's construction methods, the innovation of this paper is proposing a method for constructing urban driving cycle based on simulated annealing algorithm. The major task is the data processing and optimizing. For data processing, the characteristic parameter of the micro-trips is selected according to the theory of micro-trips analysis, then this paper performs principal component analysis to reduce the dimensions of motion characteristic parameters and the K-means clustering method is used to classify kinematics segments. In the selection of fragments, this paper adopts the simulated annealing algorithm to optimize. The final analysis results show that the error is largely reduced and the accuracy of the operating conditions is further improved.