Abstract
Use of Li-Ion batteries is increasing exponentially. 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). Using an original event-driven approach, the SoH is approximated. Comparison of the designed system is performed with traditional equivalents. Results show an outperformance of 4.7-fold in terms of compression gain and computational efficiency while maintaining sufficient precision of the SoH estimation.
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