{"title":"基于改进BP神经网络的锂电池荷电状态估计研究","authors":"Y. Hu, Zhiping Wang","doi":"10.1109/ISNE.2019.8896605","DOIUrl":null,"url":null,"abstract":"In this paper, the state of charge (SOC) is estimated by charging the battery at constant current and voltage, collecting the current, voltage, temperature and internal resistance during the experiment. The influence of internal resistance on SOC prediction of lithium batteries is mainly considered. In this paper, the Improved BP neural network is used to carry out simulation experiments. The experimental results show that the prediction accuracy is higher and the simulation effect is better than that without considering the internal resistance.","PeriodicalId":405565,"journal":{"name":"2019 8th International Symposium on Next Generation Electronics (ISNE)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Study on SOC Estimation of Lithium Battery Based on Improved BP Neural Network\",\"authors\":\"Y. Hu, Zhiping Wang\",\"doi\":\"10.1109/ISNE.2019.8896605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the state of charge (SOC) is estimated by charging the battery at constant current and voltage, collecting the current, voltage, temperature and internal resistance during the experiment. The influence of internal resistance on SOC prediction of lithium batteries is mainly considered. In this paper, the Improved BP neural network is used to carry out simulation experiments. The experimental results show that the prediction accuracy is higher and the simulation effect is better than that without considering the internal resistance.\",\"PeriodicalId\":405565,\"journal\":{\"name\":\"2019 8th International Symposium on Next Generation Electronics (ISNE)\",\"volume\":\"172 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 8th International Symposium on Next Generation Electronics (ISNE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISNE.2019.8896605\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 8th International Symposium on Next Generation Electronics (ISNE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISNE.2019.8896605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on SOC Estimation of Lithium Battery Based on Improved BP Neural Network
In this paper, the state of charge (SOC) is estimated by charging the battery at constant current and voltage, collecting the current, voltage, temperature and internal resistance during the experiment. The influence of internal resistance on SOC prediction of lithium batteries is mainly considered. In this paper, the Improved BP neural network is used to carry out simulation experiments. The experimental results show that the prediction accuracy is higher and the simulation effect is better than that without considering the internal resistance.