A Proficient Li-Ion Batteries State of Health Assessment Based on Event-Driven Processing

Abstract

Recently, the Li-Ion batteries are extensively employed. To assure effective battery utilization the Battery Management Systems (BMSs) are used. Recent BMSs are becoming sophisticated and consequently cause a higher consumption overhead. To enhance the BMSs effectiveness, this work employs event-driven sensing and processing. In contrast to the traditional counterparts, the battery cell parameters are no more captured periodically but are acquired based on events. It results in significant real-time data compression. Afterward, this non-uniformly partitioned information is employed by an original algorithm for a real-time determination and calibration of the cell State of Health (SoH). 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.