Research Projects

The rapid adoption of electric vehicles (EVs) around the world has created an urgent need for a secure, scalable & efficient smart charging infrastructure. Compared to traditional systems, smart chargers can achieve remote monitoring, time-of-use billing, & dynamic load control by integrating with the grid & cloud platforms. While these features improve the sustainability & availability of charging, also cyber security risks are introduced. Cyber-attacks intercept & tampers with the communication between the EV & the charger, software running on the charger or car can be modified to change how charging works or add malware, information like charging locations, times & power usage may be compromised. Therefore, securing this critical infrastructure is paramount for the successful & widespread adoption of EVs. Rationale: The project aims to develop & validate robust security protocols & create mechanisms to protect against cyber threats targeting EV charging stations, which are increasingly vulnerable due to their dependence on connected systems. The research will investigate potential vulnerabilities & develop mitigation strategies based on theoretical analysis, simulation & practical experimentation. Scientific Objectives: (i)Analyse security risks in various EV charging stations, communication protocols & control systems (ii) Design & implement secure methodologies for data sharing between charging stations, vehicles& grid, ensuring data confidentiality, integrity & authenticity, (iii) Develop & integrate intrusion detection systems (IDS) that can detect & respond to cyberattacks in real-time, (iv) Assess the effectiveness of various security measures, including encryption, firewalls & access control mechanisms for mitigation of cyber threats (v)Integrate secure communication protocols, IDS& mitigation strategies into a comprehensive & safe EV charging ecosystem. Hypotheses/Models to be Tested: (i)The implementation of strong encryption & authentication protocols can significantly reduce the risk of unauthorized access & data breaches in EV charging systems(ii) Anomaly detection techniques based on machine learning can effectively identify malicious activities & cyberattacks targeting EV charging stations. (iii) A simulation & real time model of the EV charging ecosystem will be developed to test the effectiveness of different security protocols & mitigation strategies under various attack scenarios. Main Experiments: (i)Conduct a comprehensive vulnerability analysis of different EV charging station architectures & communication protocols, (ii) Design & implement secure communication protocols using technologies & implement them in a testbed environment. Test the protocols for performance & security against known attacks, (iii) Develop an IDS using machine learning techniques to detect anomalies & malicious activities. Train the IDS on simulated attack data & evaluate its performance on real-world charging station data (iv) Implement various mitigation strategies, such as firewalls, intrusion detection systems, & access control mechanisms, & evaluate their effectiveness in mitigating different types of cyberattacks, (v) Integrate the secure communication protocols, IDS & mitigation strategies into a comprehensive & secure EV charging ecosystem. Application: This research will contribute to a deeper understanding of cybersecurity risks associated with EV charging infrastructure & the effectiveness of different mitigation strategies. The outcomes will provide practical guidance & tools for developing secure EV charging systems, promoting wider adoption of EVs & contributing to a more sustainable transportation system. The project will help ensure reliability & trustworthiness of EV charging, reducing range anxiety & encouraging the transition to electric vehicles. The research findings will be valuable for EV charging station manufacturers, grid operators & policymakers.