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Machine Learning And Iot-Based Li-Ion Battery Cloud Monitoring System For 5g Base Stations

Author

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  • XUEGUANG LI

    (School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan 453000, P. R. China§Intelligent Industry Big Data Application Engineering, Technology Research Center of Xinxiang, Xinxiang, Henan 453000, P. R. China¶Virtual Reality and Systems Key Laboratory of Xinxiang, Xinxiang, Henan 453000, P. R. China)

  • BIFENG LI

    (��School of Computer Science and Technology, Huanggang Normal University, Huanggang 438000, P. R. China)

  • SUFEN GUO

    (��School of Fine Arts, Xinxiang University, Xinxiang, Henan 453000, P. R. China)

  • ZHANFANG SUN

    (School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan 453000, P. R. China)

  • QIANQING WANG

    (School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan 453000, P. R. China)

  • TONGTONG DU

    (School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan 453000, P. R. China∥Manufacturing IoT Big Data Engineering, Technology Research Center of Henan Province, Xinxiang, Henan 453000, P. R. China)

  • PENG LIN

    (*China Research Institute of Radiowave Propagation, XinXiang, Henan 453000, P. R. China)

  • DONGFANG ZHANG

    (School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan 453000, P. R. China∥Manufacturing IoT Big Data Engineering, Technology Research Center of Henan Province, Xinxiang, Henan 453000, P. R. China)

Abstract

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.

Suggested Citation

  • Xueguang Li & Bifeng Li & Sufen Guo & Zhanfang Sun & Qianqing Wang & Tongtong Du & Peng Lin & Dongfang Zhang, 2023. "Machine Learning And Iot-Based Li-Ion Battery Cloud Monitoring System For 5g Base Stations," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-15.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x23401102
    DOI: 10.1142/S0218348X23401102
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