The article presents an innovative methodology for the design and validation of monitoring and anomaly detection algorithms, focused on the aging phenomenon linked to the anomalous modification of the \(R_{{ds_{on} }}\) in the switching devices of electronic systems of power in high-performance electric vehicles. The case study concerns an electric traction system with a three-phase axial flux synchronous motor integrated into a drive wheel (Elaphe) and a high-efficiency three-phase inverter with SiC (silicon carbide) technology. The methodology is developed in four phases: 1) creation of a real-time model of electric traction, validated with experimental data from WLTP tests; 2) Generation of a virtual dataset representing aging, via anomaly injection emulating the phenomenon with a scaling factor based on the \(R_{{ds_{on} }}\) on the motor phase current; 3) Design of an estimator of the \(R_{{ds_{on} }}\) using an Artificial Neural Network (ANN) regression model, including feature extraction and reduction techniques. 4) Experimental validation of the method through PIL (Processor-In-the-Loop) tests, integrating the monitoring algorithm on the NXPs32k144 embedded platform, making it interact with the electric traction model with anomaly injection.