Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Automotive Engineering
Committee Chair/Advisor
Behnaz Papari
Committee Member
Robert Prucka
Committee Member
Benjamin Lawler
Committee Member
Christopher S. Edrington
Committee Member
Gokhan Ozkan
Abstract
The electric drive system (EDS) in electric vehicles (EVs) is one of the key safety-critical components. As IoT-enabled communication infrastructure for modern cyber-physical automotive systems continues to evolve, the importance of securing EDS against cyber threats along with physical faults, has become increasingly prominent. Among physical faults, power switches are particularly vulnerable and exhibit the highest susceptibility to open-circuit faults (OCFs). A compromised EDS, whether due to cyber threats or physical issues, can lead to excessive mechanical vibrations, increased thermal stress, fluctuations in electromagnetic torque, and elevated total harmonic distortion. These factors can substantially undermine traction control stability and jeopardize occupant safety. Most existing data-driven diagnostic schemes for motor drives solely rely on residual signals and are prone to misclassification due to overlapping data features associated with these anomalies. These methods concentrate on detecting either cyberattacks or OCFs independently, lacking a unified approach that can be universally applied to both types of anomalies. Moreover, following the detection and classification of cyber and physical anomalies (CPAs), it is essential to implement a robust control mechanism that not only localizes faults but also mitigates cyber threats. This mechanism should act in response to the detection signals generated by the CPAs, thereby enhancing the overall security and operational reliability of the EDS.
So to address these issues in this dissertation, an in-depth analysis is initially conducted on how these anomalies affect the electrical, mechanical, and thermal characteristics of the EDS. Subsequently, a unified physics-informed machine learning (PIML) approach is introduced, leveraging non-residual stator voltage features to effectively detect and differentiate between cyberattacks and OCFs. Moreover, the performance of the proposed PIML framework is compared against traditional residual-based methods using various machine learning models. When an OCF detection signal is identified through the PIML model, the corresponding resistive-loss profile for each semiconductor device, derived from the active thermal management-based model predictive control of the EDS, is used to accurately localize the OCF. Moreover, a reliability score quantification criterion is employed for machine learning model selection, which considers both traditional performance metrics and timing characteristics. Conversely, upon detecting a cyberattack, an AI-based reference tracking model, comprising signal regulation and prediction components, is deployed to mitigate its impact on the normal operation of the EDS. Thus, the integral components of detection, differentiation, localization, and mitigation of CPAs form a robust and comprehensive Resilient Control Framework (RCF). This framework is designed to enhance the reliability of EDS operations, ensuring optimal performance even under various abnormal conditions.
Finally, a trust-based monitoring system is proposed to evaluate the performance integrity of the EDS by emulating human-like decision-making. To achieve this, a model-based trust evaluation framework is developed to compute a trust score based on the severity and impact of potential cyber and physical disturbances. This framework aims to quantify the system’s performance under both normal and anomalous operating conditions.
Recommended Citation
Arsalan, Ali, "Resilient Control Framework for EV Motor Drive System Subject to Cyber-Physical Security" (2025). All Dissertations. 3983.
https://open.clemson.edu/all_dissertations/3983