Estimation of Lead Acid Battery Degradation – A Model for the Optimisation of Battery Energy Storage System using Machine Learning

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.