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Despite substantial advancements in autonomous Electric Vehicles (EVs), three critical questions remain unanswered: How can an EV digital twin be modeled to simulate realistic observation data and attack profiles? What are the most effective state estimation techniques for ensuring resilience in autonomous EV systems? How can these developments impact not only EV operations but also broader communities and society? This framework addresses these challenges by developing a comprehensive simulation for EV state estimation under cyberattacks, evaluating the performance of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) techniques in both ideal and attack scenarios.
Key contributions include proposing a realistic EV simulation incorporating dynamic models and cyberattack profiles, evaluating the resilience of EKF, UKF, and PF against False Data Injection (FDI) and Denial of Service (DoS) attacks, and highlighting the societal benefits of improved state estimation, fostering safer and more trustworthy EV systems.
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IEEE/ICACT20250208 Slide.05
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