MACHINE LEARNING AND IOT-BASED LI-ION BATTERY CLOUD MONITORING SYSTEM FOR 5G BASE STATIONS

被引:2
作者
Li, Xueguang [1 ,4 ,5 ]
Li, Bifeng [2 ]
Guo, Sufen [3 ]
Sun, Zhanfang [1 ]
Wang, Qianqing [1 ]
Du, Tongtong [1 ,6 ]
Lin, Peng [7 ]
Zhang, Dongfang [1 ,6 ]
机构
[1] Henan Inst Technol, Sch Comp Sci & Technol, Xinxiang 453000, Henan, Peoples R China
[2] Huanggang Normal Univ, Sch Comp Sci & Technol, Huanggang 438000, Peoples R China
[3] Xinxiang Univ, Sch Fine Arts, Xinxiang 453000, Henan, Peoples R China
[4] Intelligent Ind Big Data Applicat Engn Technol Res, Xinxiang 453000, Henan, Peoples R China
[5] Virtual Real & Syst Key Lab Xinxiang, Xinxiang 453000, Henan, Peoples R China
[6] Mfg IoT Big Data Engn Technol Res Ctr Henan Prov, Xinxiang 453000, Henan, Peoples R China
[7] China Res Inst Radiowave Propagat XinXiang, XinXiang 453000, Henan, Peoples R China
关键词
Machine Learning; Internet of Things; LSTM; Battery; 5G; HEALTH ESTIMATION; STATE; CHARGE;
D O I
10.1142/S0218348X23401102
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.
引用
收藏
页数:15
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