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With the increase in the number of electric vehicles (EV), there is a needfor the development of the EV charging infrastructure (EVCI) to facilitate fastcharging, thereby mitigating the EV congestion at charging stations. The roleof the public charging station depot is to charge the vehicle, prioritizing theachievement of the desired state of charge (SoC) value for the EV battery orcharging till the departure of the EV, whichever occurs first. The integrationof cyber and physical components within EVCI defines it as a cyber physicalpower system (CPPS), increasing its vulnerability to diverse cyber attacks.When an EV interfaces with the EVCI, mutual exchange of data takes place viavarious communication protocols like the Open Charge Point Protocol (OCPP), andIEC 61850. Unauthorized access to this data by intruders leads to cyberattacks, potentially resulting in consequences like energy theft, and revenueloss. These scenarios may cause the EVCI to incur higher charges than theactual energy consumed or the EV owners to remit payments that do notcorrespond adequately to the amount of energy they have consumed. This articleproposes an EVCI architecture connected to the utility grid and uses the EVCIdata to identify the anomalies or outliers present in the EV transmitted data,particularly focusing on SoC irregularities.

Regression Based Anomaly Detection in Electric Vehicle State of Charge Fluctuations Through Analysis of EVCI Data