Event-Driven Sampling Based Li-Ion Batteries SoH Estimation in the 5G Framework

Abstract

The digitization and IoT advancement are evolving the energy sector. 5G is playing an important role in connecting various smart grid modules and stockholders. Batteries are elementary parts of smart grids and are extensively employed. To assure an effective utilization and longer life the Battery Management Systems (BMSs) are employed. Recent BMSs are becoming sophisticated and consequently cause a higher consumption overhead on the battery. To enhance the BMSs power efficiency, this work employs event-driven sensing and processing. In contrast to the traditional counterparts, the battery cell parameters like voltages and currents are no more captured periodically but are acquired based on events. It results in significant real-time data compression. Afterward, a novel algorithm, for a real-time determination of the State of Health (SoH), employs this non-uniformly partitioned information. The estimated SoH is calibrated by using an original event-driven approach. The devised system comparison is made with the traditional counterparts. Results demonstrate a more than third order of magnitude outperformance in terms of compression gain and computational efficiency while assuring an analogous SoH estimation precision. Thanks to the 5G network, findings are effectively logged on the cloud for further analysis and decision making.