{"title":"基于DAREKF的锂电池荷电状态联合估计","authors":"Kun-ye Zhou, Chunyang Zhang, Jiaqi He","doi":"10.1117/12.2680030","DOIUrl":null,"url":null,"abstract":"The power source of electric vehicle is lithium-ion battery. Due to the influence of aging degree, the driver's estimation error of lithium-ion battery power is caused, so it is very practical to accurately estimate its state of charge. In order to solve the problems of Gaussian white noise and poor robustness of adaptive extended Kalman filter algorithm, this paper adopts double adaptive robust extended Kalman filter algorithm for online joint estimation of model parameters and SOC. The simulation results show that, compared with AEKF estimation, the maximum absolute error, mean absolute error and root mean square error of battery state estimation can be reduced by 1.14%, 0.49% and 0.62% respectively.","PeriodicalId":201466,"journal":{"name":"Symposium on Advances in Electrical, Electronics and Computer Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Joint estimation of SOC for lithium batteries based on DAREKF\",\"authors\":\"Kun-ye Zhou, Chunyang Zhang, Jiaqi He\",\"doi\":\"10.1117/12.2680030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The power source of electric vehicle is lithium-ion battery. Due to the influence of aging degree, the driver's estimation error of lithium-ion battery power is caused, so it is very practical to accurately estimate its state of charge. In order to solve the problems of Gaussian white noise and poor robustness of adaptive extended Kalman filter algorithm, this paper adopts double adaptive robust extended Kalman filter algorithm for online joint estimation of model parameters and SOC. The simulation results show that, compared with AEKF estimation, the maximum absolute error, mean absolute error and root mean square error of battery state estimation can be reduced by 1.14%, 0.49% and 0.62% respectively.\",\"PeriodicalId\":201466,\"journal\":{\"name\":\"Symposium on Advances in Electrical, Electronics and Computer Engineering\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Symposium on Advances in Electrical, Electronics and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2680030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Symposium on Advances in Electrical, Electronics and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2680030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Joint estimation of SOC for lithium batteries based on DAREKF
The power source of electric vehicle is lithium-ion battery. Due to the influence of aging degree, the driver's estimation error of lithium-ion battery power is caused, so it is very practical to accurately estimate its state of charge. In order to solve the problems of Gaussian white noise and poor robustness of adaptive extended Kalman filter algorithm, this paper adopts double adaptive robust extended Kalman filter algorithm for online joint estimation of model parameters and SOC. The simulation results show that, compared with AEKF estimation, the maximum absolute error, mean absolute error and root mean square error of battery state estimation can be reduced by 1.14%, 0.49% and 0.62% respectively.
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