Li-Ion Battery SoH Estimation Based on the Event-Driven Sampling of Cell Voltage

Abstract

In modern grids the deployment of rechargeable batteries is exponentially increasing. The Battery Management Systems (BMSs) are used to achieve a longer battery life and to maximize its usefulness. Contemporary BMSs are complex, creating a greater overhead consumption on the battery. The purpose of this work is to improve the power efficiency of the modern BMSs. To this end the processes of level-crossing sensing and processing are used. The emphasis is on developing a reliable, efficient, and real-time technique for estimating battery cells’ state of health (SoH) by measuring their instantaneous voltages. Using an original event-driven approach, the SoH is approximated. Comparison of the designed system is performed with traditional counterpart. Results show, for the case of a 2 cells battery pack, an outperformance of 21.2 folds in terms of compression gain and computational efficiency while maintaining sufficient precision of the SoH estimation.