A Study on Compensation for Operating Region Variations in an In-Wheel PMSM Under Temperature Changes Using Neural Network Algorithms

This study proposes a compensation method for operating region variations in in-wheel PMSMs, which are widely used in small mobility applications such as e-scooters and e-bikes. As motor temperature increases during operation, electrical parameters such as inductance vary, leading to unstable control. To address this, a Single-Layer Backpropagation Neural Network (SLBPNN) is used to estimate inductance variations in real-time. The proposed algorithm adjusts the motor’s operating point to maintain stable performance under thermal stress. Simulation results using MATLAB 2024b confirm the model’s effectiveness by estimating inductance from voltage, current, speed, and position inputs. Experimental validation further demonstrates that the proposed method compensates for the shift in the operating region due to temperature changes, improving the overall motor efficiency.