Barre, U.P.V.Satyanarayan, S.Reddy, H.Pulikala, A.Bajaj, A.2026-02-0620232023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, 2023, Vol., , p. -https://doi.org/10.1109/ICCCNT56998.2023.10308356https://idr.nitk.ac.in/handle/123456789/29369Electric cars offer numerous benefits and are considered the future of the automobile industry. However, their worldwide adoption still needs to grow. One of the primary reasons for this delay in electrification is charge anxiety, which refers to the uncertainty customers feel when connecting the charging cable to the car. To address this issue, this study analyses the performance of the charging system using a machine learning model to identify sensitive signals that influence the charging process and can cause successful charging or charge termination. The analysis will also help to define robust operating regions where the charging component can reliably function, regardless of external conditions. This study's findings will provide insights into electric vehicle charging behavior with the supply station. © 2023 IEEE.Digital InteroperabilityElectric Vehicle (EV)EV chargingMachine LearningRandom ForestRobustness