Modeling Battery Energy Storage Systems Based on Remaining Useful Lifetime through Regression Algorithms and Binary Classifiers

Abstract

This research work implements an initial methodology for the assessment of Battery Energy Storage Systems (BESSs) based on Remaining Useful Lifetime (RUL), and its main contribution is the modeling and estimation of Health and Charge indicators through regression algorithms and binary classifiers during the battery’s operation. Linear Regression, Ridge Regression, and Lasso Regression are the main algorithms for modeling the State of Health (SOH), while Decision Tree, Naïve Bayes, and Logistic Regression are implemented as binary classifiers to estimate the charge and discharge during battery operation. Additional data science techniques are executed to provide feature selection, validation, and metrics of performance. The results show that binary classifiers achieve a remarkable accuracy, around 95% for charge and discharge predictions, which is supported by experimental battery measurements. Similarly, regression algorithms achieve accuracy results around 97% and provide a basis for determining the Remaining Useful Lifetime (RUL) according to the End-of-Life (EOL) criteria of a BESS.