Machine Learning for Estimation of State-of-Charge of Energy Storage System

Abstract

This paper presents a long short-term memory (LSTM) network for battery state-of-charge (SoC) estimation. At present, there is limited research on machine learning techniques for the SoC estimation of batteries in grid applications. Therefore, this paper studies the use of the LSTM network for battery SoC estimation during peak demand reduction. The LSTM network is compared with other existing SoC estimation methods such as empirical method, coulomb counting, extended Kalman filter, and unscented Kalman filter, along with another machine learning algorithm, namely the feedforward neural network. The LSTM network achieves an average mean absolute error of 0.10 and a root mean square error of 0.12.