Assessment of Battery Degradation Using Rainflow Cycle-Counting Algorithm

Battery based energy storage systems are increasingly popular in power systems as renewable energy continues to grow while ensuring the reliability of power supply. However, battery degradation is a significant issue that can impact power system operations and optimal scheduling strategies. Therefore, estimating the remaining life cycle or assessing the health of batteries due to the degradation process has become a new challenge and research focus in various engineering fields. This topic is relevant in the context of electric vehicles (EVs), where battery degradation caused by continuous and non-continuous operations (i.e., charging and discharging cycles). Degradation can limit the performance of batteries and occur throughout their lifespan whether they are in use or not. The degradation process is complex and influenced by usage and external conditions that are normally measured by state of health (SOH). Therefore, predicting the SOH of batteries is crucial in ensuring the safety, stability, and long-term viability of energy storage and EVs systems. This prediction requires a battery mechanism model that can be established from a complex electrochemical process. Alternatively, a rainflow cycle-counting algorithm (RCCA) has become popular among researchers for battery degradation estimation because of its simplicity. This paper presents a comprehensive review of the battery degradation estimation using RCCA to count the equivalent cycles of charging and discharging profiles.