Enhanced Real-Time ATM-based MPC for Electric Vehicles with Cyber-Physical Security Aspect

Inverter-based electric drive systems (EDS) in electric vehicles (EVs) are susceptible to various common cyber and physical anomalies (CPAs), such as cyber-attacks (CAs) and open-circuit faults (OCFs) in power switches. Most existing data-driven diagnostic schemes for motor drives, solely rely on residual and 3-phase output current signals and are prone to misclassification due to overlapping data features associated with these anomalies. Moreover, these methods concentrate on detecting either CAs or OCFs independently, lacking a unified approach that can be universally applied to both types of anomalies. Therefore, in this work, a physics-informed machine learning (PIML) approach is proposed which can effectively distinguish between CAs and OCFs in EDS. In this regard, the unique features associated with stator current, resulting from various CPAs, are used to estimate the stator voltage characteristics through a synchronous motor mathematical model. The proposed voltage-based features characterizing better correlation via distinctive EDS transients in response to these anomalies are further used as the system’s prior information by a machine learning (ML) classifier. Moreover, active-thermal management (ATM)-based model predictive control (MPC) is utilized in the control layer due to its benefits in device-level thermal cycling and power quality. In addition, various variations in electrical, mechanical, and thermal characteristics due to CPAs in EDS have also been analyzed. The presented approach is evaluated for the US06 standard drive cycle in real-time via controller-hardware-in-the-loop (CHIL) experiment for various case study scenarios, resulting in a classification accuracy of 98.92%.