## 2 Background  
### Related Works  
Existing research works have utilized data-driven approaches and clustering solutions for characterizing EV charging behavior. [PERSON] et al. [19] utilized the K-means algorithm to find clusters of charging behavior. Three clusters were obtained using features that included connection time and departure time. Based on simulation results, two of the three cluster groups were identified as stable and predictable users whereas the third cluster was identified as unpredictable users. K-nearest neighbor (K-NN) algorithm was then used to classify these two groups. Similarly, four clusters of charging behavior were identified using hierarchical clustering in [20]. Most of the users belonged to the first cluster, where the common charging behavior was to charge during the night and in the morning. The users in the second cluster preferred to charge after work during the evening. The third cluster users charged throughout the day and the fourth cluster users charged in the late evening. [PERSON] et al. [21] discovered thirteen clusters of behavior using the Gaussian mixture model. Seven of these clusters contained daytime charging sessions with varying lengths of charging, and six of them contained overnight charging sessions. They also utilized k-medoids clustering to find nine distinct clusters. Among these, three clusters contained daytime chargers, three contained overnight chargers, and three contained non-typical groups of chargers. In [22], the authors proposed a machine learning approach to learn charging behavior by considering different time granularity. Two independent models were first developed to predict the registered users and unregistered users, respectively. The results were then combined to make the final predictions. Extreme gradient boosting (XGBoost) performed the best and the prediction accuracy was the highest with a 15-min granularity. Similarly, [23] utilized a machine learning approach to predict charging behavior with added weather, traffic, and events information to the historical charging data. They reported the best performance with an ensemble model which outperformed existing approaches. [PERSON] et al. [15] utilized real-world charging data from Germany to explain temporal and power-specific flexibility characteristics of three vehicle fleets namely pool vehicles of office employees, a public authority, and a logistics company. Their analysis revealed a variability in charging behavior among the fleets with a higher charging flexibility in the logistics group.  
From the related works in the literature, only one research work has focused on EV impacts during COVID-19. [PERSON] et al. [24] studied the impacts of COVID-19 on the Chinese EVs industry. Various economic trends in EV sales were discovered including a short-term reduction of sales during COVID-19. They also identified an interruption of materials import due to travel restrictions leading firms to domestic production of critical EV parts. Although previous works in the literature have addressed EV charging behavior, they are based on pre-pandemic conditions. Therefore, to better understand user charging compartment during COVID-19 and pandemics or lockdown scenarios in general, it is essential to analyze and model the data during the pandemic period.