The state-of-charge predication of lithium-ion battery energy storage system using data-driven machine learning

Abstract

Accurate estimation of state-of-charge (SOC) is critical for guaranteeing the safety and stability of lithium-ion battery energy storage system. However, this task is very challenging due to the coupling dynamics of multiple complex processes inside the lithium-ion battery and the lack of measure to monitor the variations of a battery’s internal properties. Recently, with the continuous development of Graphic Processing Unit (GPU) computing power, there is an increasing interest in applying deep-learning as SOC estimation approaches. In this paper, a novel SOC estimation scheme for lithium-ion energy storage system is proposed based on Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) neural network. This method is completely driven by the actual operating data from a photovoltaic energy storage system without using any artificial battery models or inference systems. Compared with traditional SOC estimation methods, the CNN-LSTM model can overcome the deviation in estimation caused by voltage jump at the end of charge and discharge, provide satisfied SOC estimation results during stabilized stage and various charging/discharging stages of the assembled lithium-ion batteries in the system. The calculation results indicate that this method enables fast and accurate SOC estimation with an RMSE of less than 0.31% over the entire operating data of the photovoltaic energy storage system for a full day.