Trust Evaluation Framework for Adaptive Load Optimization in Motor Drive System

Electric drive systems (EDSs) are vital for automotive and industrial applications but remain highly vulnerable to cyber and physical anomalies (CPAs), such as inverter open-circuit faults, sensor failures, and malicious cyberattacks. Ensuring reliable EDS operation requires the controller to receive accurate and uncompromised feedback and reference signals continuously. However, many existing data-driven detection and mitigation strategies rely on large training datasets, impose significant computational overhead, and often lose effectiveness under various abnormal operating conditions. To overcome these limitations, this paper introduces a trust evaluation framework that continuously assesses the reliability of all incoming signals to the EDS controller by combining behavioral analysis with historical reliability records. The proposed scheme offers a lightweight and model-independent approach, enabling reliable, adaptive decision-making by leveraging both current and historical signal behavior. To this end, this paper further integrates the resulting trust values into a torque-split optimization algorithm, enabling adaptive load optimization by dynamically reducing the torque contribution of motors operating under abnormal or low-trust conditions, thereby demonstrating clear applicability for automotive drive systems. The framework is validated in a real-time OPAL-RT environment across multiple CPA scenarios, demonstrating accurate anomaly detection and adaptive torque redistribution. Owing to its simplicity and versatility, the proposed method can be readily extended to other safety-critical drive applications.