Modeling lithium-ion Battery in Grid Energy Storage Systems: A Big Data and Artificial Intelligence Approach

Grid energy storage system (GESS) has been widely used in smart homes and grids, but its safety problem has impacted its application. Battery is one of the key components that affect the performance of GESS. Its performance and working conditions directly affect the safety and reliability of the power grid. With the development of data analytics and machine learning, the accuracy and adaptability of the battery state estimation model can be greatly improved. This paper proposes a new method to model battery, with low-quality data. First, it designs a data cleaning method for GESS battery operating data, including missing data filling and outlier data repair. Then, the repaired data is used to model battery. A battery mathematical model is proposed based on a deep learning algorithm to realize accurate GESS state estimation. The performance of the developed deep learning method is compared with conventional BP neural network and generalized regression neural network to highlight the technical merits. Results derived in this paper provide a solid basis for high-efficiency GESS operation and energy management.