## 5 Conclusions and Future Work  
This paper presented an analysis of public EV charging behavior during the COVID-19 pandemic. Analysis indicates that the lockdown measures had an impact on the charging activity during the pandemic. It was found that there were no charging sessions between the beginning of August and until the middle of November 2020. Despite the recent growth in charging activity due to increased testing, vaccination, and reduction in lockdown restrictions, the charging activity is currently about half of pre-pandemic. Moreover, the charging behavior evolved with shorter and high-energy sessions during the pandemic. Furthermore, using clustering algorithms, three distinct groups of charging behaviors were identified. The best clustering was obtained using K-means followed by hierarchical clustering. The limitations of the clustering approach include the single method used for determining the number of clusters as well as the reliance on only two parameters for clustering. The proposed work contains several practical implications. The analysis of the public charging behavior during the pandemic and lockdown situations, in general, helps the energy sector and policymakers to better manage the charging demand. Based on the identified trends and patterns, the charging electricity price of use could also be adjusted to control some of the charging load.  
For future work, an analysis of residential charging data should be performed. It is likely to show a dramatic increase in charging activities due to the lockdown measures forcing people to charge their vehicles residentially. For optimal decision-making on EV charging demand on a state level, it is essential to combine both residential and non-residential charging activities over a period of time. Moreover, data from other charging facilities in the USA and across the globe should be used to further compare and validate the findings in this work. Although beneficial, the proposed research is limited by the data source which only characterizes a single location. Given that the pandemic is still ongoing with new variants being identified, a forecast for the year 2022 based on the current data is required. The forecasting model should incorporate statistical, time series, and machine learning approaches. Finally, deep learning and neural approaches to clustering can be experimented with to provide a further comparison. Deep learning-based methods are known to usually provide more accurate solutions with larger datasets and therefore must be explored in future research.  
Conceptualization, S.S. and A.R.A.-A.; Data curation, S.S.; Methodology, S.S. and A.R.A.-A.; Supervision, A.R.A.-A.; Writing--original draft, S.S.; Writing--review & editing, A.R.A.-A. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clustering/Metrics** & **Silhouette** & **Davies-Bouldin** & **Calinski-Harabasz** \\ \hline K-means & 0.41 & 0.85 & 2904 \\ Hierarchical & 0.38 & 0.74 & 2270 \\ GMM & 0.15 & 2.05 & 344 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Internal validation scores.  
**Funding:** This work was supported in part by the Open Access Program from the American University of Sharjah under Grant OAP-CEN-069.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset can be accessed using: [[https://ev.caltech.edu/dataset](https://ev.caltech.edu/dataset)]([https://ev.caltech.edu/dataset](https://ev.caltech.edu/dataset)) (accessed on 1 June 2020).  
**Acknowledgments:** This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah.  
**Conflicts of Interest:** The authors declare no conflict of interest.