Temperature Drift Correction of Hall-Effect Current Sensors via Harris Hawks Optimization Enhanced Surface Modeling

Hall-effect current sensors have been widely used due to their fast response, excellent linearity, and inherently noncontact measurement capability. They play an irreplaceable role in various critical electrical applications such as non-destructive testing and condition monitoring. However, due to the intrinsic properties of the semiconductor Hall element, these sensors are susceptible to ambient temperature variations, resulting in significant temperature drift that compromises accuracy. As a result, the measurement accuracy of Hall-effect sensors still has room for improvement, and robust calibration of their output is essential. In this study, a novel temperature drift correction method for Hall-effect current sensors is proposed, which combines a precise surface modeling approach with the Harris Hawks Optimization (HHO) algorithm to optimize the compensation model parameters and improve calibration performance across a wide temperature range. The effectiveness of the proposed method is rigorously experimentally validated. After correction, the maximum measurement error was dramatically reduced to only $3.35 \%$ of the original uncompensated value. The results demonstrate that the proposed approach significantly enhances the measurement accuracy and robustness of Hall-effect current sensors under varying temperature conditions, making it highly suitable for demanding industrial applications.