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This presentation discusses the integration of advanced algorithms in Electric Vehicle (EV) systems for navigation, sensors, actuators, and energy management, highlighting their vulnerability to cybersecurity threats that can affect performance, privacy, and safety. A major issue is the susceptibility to cyberattacks like False Data Injection (FDA) and Denial of Service (DoS), which can disrupt navigation and control, leading to accidents and reduced efficiency. The research models EV digital twin systems under attack using simulations and evaluates the proposed algorithms for accuracy, safety, and efficiency in state estimation. It provides an in-depth analysis of FDA and DoS attacks and assesses the performance of three robust algorithms: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). Simulation results indicate that EKF and UKF significantly enhance the resilience of EV systems compared to PF, improving accuracy and safety under attack scenarios. These findings contribute to academia and industry by addressing real-time EV cybersecurity challenges and offering insights into advanced filtering techniques and their applications.
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IEEE/ICACT20250208 Slide.03
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