V. Duong, H. A. Bastawrous, K. Lim, K. See, P. Zhang, S. Dou
{"title":"基于多自适应遗忘因子的递归最小二乘估计电动汽车磷酸铁锂电池荷电状态","authors":"V. Duong, H. A. Bastawrous, K. Lim, K. See, P. Zhang, S. Dou","doi":"10.1109/ICCVE.2014.7297603","DOIUrl":null,"url":null,"abstract":"This work presents a novel technique which is simple yet effective in estimating electric model parameters and state-of-charge (SOC) of the LiFePO4 battery. Unlike the well-known recursive least-squares-based algorithms with single constant forgetting factor, this technique employs multiple adaptive forgetting factors to provide the capability to capture the different dynamics of model parameters. The validity of the proposed method is verified through experiments using actual driving cycles.","PeriodicalId":171304,"journal":{"name":"2014 International Conference on Connected Vehicles and Expo (ICCVE)","volume":"35 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"SOC estimation for LiFePO4 battery in EVs using recursive least-squares with multiple adaptive forgetting factors\",\"authors\":\"V. Duong, H. A. Bastawrous, K. Lim, K. See, P. Zhang, S. Dou\",\"doi\":\"10.1109/ICCVE.2014.7297603\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a novel technique which is simple yet effective in estimating electric model parameters and state-of-charge (SOC) of the LiFePO4 battery. Unlike the well-known recursive least-squares-based algorithms with single constant forgetting factor, this technique employs multiple adaptive forgetting factors to provide the capability to capture the different dynamics of model parameters. The validity of the proposed method is verified through experiments using actual driving cycles.\",\"PeriodicalId\":171304,\"journal\":{\"name\":\"2014 International Conference on Connected Vehicles and Expo (ICCVE)\",\"volume\":\"35 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Connected Vehicles and Expo (ICCVE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCVE.2014.7297603\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Connected Vehicles and Expo (ICCVE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCVE.2014.7297603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SOC estimation for LiFePO4 battery in EVs using recursive least-squares with multiple adaptive forgetting factors
This work presents a novel technique which is simple yet effective in estimating electric model parameters and state-of-charge (SOC) of the LiFePO4 battery. Unlike the well-known recursive least-squares-based algorithms with single constant forgetting factor, this technique employs multiple adaptive forgetting factors to provide the capability to capture the different dynamics of model parameters. The validity of the proposed method is verified through experiments using actual driving cycles.