Estimating Remaining Usable Time of Batteries With Uncertainty Quantification: A Case Study on Base Transceiver Station Application Using Real-Life Data

Systems consisting of multiple components can experience downtime during their operational lifetime due to issues with one or more of these components. For instance, accurately predicting the remaining time before a battery depletes its total energy is crucial for systems like a Base Transceiver Station. This paper addresses how long the battery will sustain a Base Transceiver Station under varying load conditions with the associated uncertainty when the external power source is interrupted. A customized approach using battery modeling and load forecasting is designed to predict the remaining usable time of the battery. In total, 7 Base Transceiver Stations are considered and clustered into 3 groups based on their installed battery capacity. First, a battery model is developed employing the modified Shepard model using real-life measured battery discharge data. Since the load currents are unknown beforehand, they are forecasted using a time series modeling approach. The resulting uncertainty from both models is then quantified using a prediction interval. Using our approach, the averaged Mean Absolute Error of the remaining usable time estimates is 12.61 minutes for all discharges. The associated uncertainty with these estimation using the 95% prediction interval is 26.43 minutes. The estimation errors are relatively small compared to true discharge times, as some batteries can provide energy close to 15 hours. The overall estimation results closely matched the measured values, providing valuable insights for proactive planning and management of potential failures.