Design of power lithium battery management system based on digital twin

Abstract

The accurate estimation of the State of Charge (SoC) of batteries has always been the focus of Battery Management System (BMS). However, the current BMS has problems such as difficult data sharing, weak data processing capability and limited data storage capacity, so the simplest ampere-time integration method is used to estimate the SoC, and the estimation results are highly biased. In this paper, an innovative digital twin-driven battery system framework is proposed and successfully developed. A joint HIF-PF online algorithm for estimating SoC under the experimental condition Beijing Bus Dynamic Stress Test (BBDST) is also proposed, which is comparable to the conventional Extended kalman filter (EKF), HIF and Particle Filter (PF). The results demonstrate the superiority of the algorithm. Then, we applied the algorithm to estimate SoC. In addition, the system implements functions such as real-time voltage and current monitoring and visualization.