Mobile Network Backup Power Supply Battery Remaining Useful Time Prediction Using Machine Learning Algorithms

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Addis Ababa University


Base transceiver stations (BTSs) in mobile cellular systems are critical infrastructure for providing reliable service to mobile users. However, BTSs can be disrupted by electric power supply interruptions, which can lead to degraded quality of service (QoS) and quality of experience (QoE) for users. The reliability of the battery used in BTSs is affected by a number of factors, including: The instability of the primary power supply, Temperature fluctuations, Battery aging, the number of charging and discharging cycles (CDC) and the depth of discharge (DOD).As a result of these factors,the state of health (SOH) of a battery is impacted which will in turn affect the remaining useful time (RUT) of the battery. This can lead to disruptions in service for mobile users, as the BTS may not have available power to operate during a power outage. To address this issue, the developed supervised machine learning (ML) techniques have predict the RUT of lithium iron phosphate (LFP) batteries installed in BTSs have used ML models and trained on data that has been extracted from power and environment (P&E) monitoring tool Net Eco(iManager NetEco data center infrastructure management system) . The ML models can then be used to predict the RUT of a battery, which can help to ensure that batteries are replaced before they fail to deliver the designed capacity. In this study, three ML models were evaluated: linear regression, random forest regression, and support vector regression. The support vector regression model provided the best overall prediction performance, with a test error of 4.85%. This suggests that the support vector regression model is a promising tool for predicting the RUT of LFP batteries used in BTSs.



CDC, DOD, RUT, QoS, QoE ….