Neural Network – based Life Health Estimation of Valve – regulated Lead Acid Batteries
Keywords:
valve – regulated lead acid batteries, coup de fouet, health estimation, useful life, nonlinear autoregressive with exogenousAbstract
Load, internal resistance, ambient temperature, and discharge cycle are identified as key influencers of battery health estimation. Notably, increasing load correlates directly with a deeper discharge, demonstrating the load's impact on battery performance. Strict adherence to operational conditions, such as float voltage thresholds established by battery manufacturers, is emphasized. Batteries in stable power grid environments with limited discharge cycles are examined in this study introducing the "coup de fouet" modeling technique for accurate battery characteristic predictions during discharges. These parameters prove highly significant in estimating the useful life health of valve–regulated lead acid (VRLA batteries) [6][8]. The research highlights the importance of comprehensive understanding among telecommunications and allied stakeholders and the necessity of meeting essential requirements, including maintaining the manufacturers' ambient temperature requirements and load monitoring to ensure battery performance remains within recommended thresholds.
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Copyright (c) 2024 Edgar Cortes, Mary Grace Cortes
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