Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge–discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge–discharge area can be summarized into seven representative profiles.

# Chemical technology # electric vehicle # clustering # data analysis # machine learning # battery

Kangsan Kim, Geumbee Kim, Jiwon Yoo, et al. (2024). "Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea". Sensors. https://doi.org/10.3390/s24216800