Work

Estimation of Lead Acid Battery Degradation—A Model for the Optimization of Battery Energy Storage System Using Machine Learning

Year: 2025

Type: article

Abstract: Energy storage systems are becoming increasingly important as more renewable energy systems are integrated into the electrical (or power utility) grid. Low-cost and reliable energy storage is paramount if renewable energy systems are to be increasingly integrated into the power grid. Lead-acid batteries are widely used as energy storage for stationary renewable energy systems and agriculture due to their low cost, especially compared to lithium-ion batteries (LIB). However, lead-acid battery technology suffers from system degradation and relatively short lifetime, largely due to its charging/discharging cycles. In the present study, we use Machine Learning methodology to estimate the battery degradation in an energy storage system. It uses two types of datasets: discharge condition and lead acid battery data. The use of the Long Short-Term Memory (LSTM) model algorithm for estimating battery Remaining Useful Lifetime (RUL) is a promising development that has the potential to significantly improve battery performance/durability and reduce maintenance costs. The initial State of Health (SOH) and predicted SOH values provide insights into battery aging and RUL estimation for lead-acid batteries in the renewable energy systems for agricultural purposes.

Source: Electrochem

DOI: 10.3390/electrochem6030033