Author:
Tao Jiaju,Qian Song,Yang Junfei
Abstract
Abstract
This study investigates the crucial role of battery capacity and charging-discharging cycles in determining the performance of communication devices, especially electric vehicles (EVs). Given the multifaceted factors affecting battery performance, including type, specification, and environmental conditions, it becomes essential to develop a robust method for modeling and analyzing battery behavior under various scenarios. Our research introduces a novel approach that amalgamates machine learning and optimization techniques to comprehensively model and analyze battery performance. Utilizing real-world data from an EV fleet, we validate our method against existing models, demonstrating superior accuracy, efficiency, and robustness. The findings reveal our method’s effectiveness in optimizing communication devices’ performance metrics, such as efficiency, cost, and reliability, by accurately capturing the nonlinear and dynamic relationships between battery capacity and usage cycles. Our conclusion underscores the potential of this method in enhancing the management and optimization of communication devices, paving the way for more sustainable and efficient use of battery technology in EVs.