State of Charge Estimation of Storage Battery in Data Center Based on Traceless Kalman Algorithm

In the context of the rapid development of data centers, this article proposes a kind of UPS battery monitoring scheme, which divides the monitoring system into the following parts: data acquisition module, data transmission module, and management coordination. According to the circuit model of battery, particle swarm optimization algorithm is used to identify the battery parameters. In order to improve accuracy of the battery state of charge estimation, an unscented Kalman filter algorithm is applied to estimate the battery SOC and proof the precision of the algorithm.

I. Background

The new generation of digital technology has become an engine for promoting high-quality economic development. At the same time, the amount of data generated by various industries has also ushered in a round of explosive growth, and the demand for data centers that assume the role of data centers and computing infrastructure is also increasing. In the development of electric power information and digitization, a large amount of electric power information data has been generated, which is related to the stable operation of various industries in society and plays a significant role in the operation and management of enterprises and social energy regulation. As an effective carrier of these data, data centers have very important practical significance in making sure the normal work of data centers in the power industry [1].