An Effective Li-Ion Battery State of Health Estimation Based on Event-Driven Processing

Abstract

Summary The most common types of rechargeable batteries are Li-ion batteries. It is important to ensure that the batteries are always in good health and thus achieve a longer lifespan. The Battery Management System (BMS) is utilized to achieve this aim. Given that a single rechargeable battery can have many cells, a BMS is becoming more complicated. The main disadvantage of having a complete BMS is that it can lead to higher power overhead consumption. Therefore there is a need to develop a BMS that does not compromise on its ability to accurately monitor power systems, but do so at low overhead consumptions. In this paper, the aim is to develop and enhance the conventional Coulomb Counting based SOH method to create a reliable, effective and real-time technique for estimating the SOH of cells. The paper also compares the developed method with its traditional counterpart, and the results of the experiment show that the new model performs better in terms of computational efficiency, compression gain, and SOH estimation accuracy.