Machine Learning-Driven Sensitivity Analysis for a 2-Layer Printed Circuit Board Inductive Motor Position Sensor

Motor position sensors are critical parts for traction motors control in electrified automotive powertrain. As motors are getting more compact due to the advance of technology the packaging space for motor position sensors is becoming increasingly restricted. This study presents a two-layer (2L) printed circuit board (PCB) routing strategy for inductive motor position sensors with limited area. A prototype was fabricated and tested on a test bench using a comprehensive design of experiments that contains 625 combinations of X- and Y-offsets, tilt angle, and airgap at various levels (±0.5 mm in X/Y, ±0.5° tilt, 1.9–3.1 mm airgap). Across the tolerance box, the accuracy under all test cases remained within ±1 electrical degree. The accuracy analysis through Fourier series on circle shows that the DC offset and magnitudes mismatch of the 3 Rx signals are the dominant error contributors due to the routing modification. An Extreme Gradient Boosting (XGBoost) model was trained and validated with R² = 0.9951 on the sensor data. The SHapley Additive exPlanations (SHAP) analysis identified tilt and Y-offset as dominant contributors to accuracy degradation. The model revealed a mild Y-axis asymmetry introduced by routing modifications. The SHAP results show that machine learning workflow provides a general, quantitative framework for analyzing inductive sensor layouts and installation tolerances.