A Wireless Charging Alignment Strategy Under Complex Working Conditions Using Voltage Fingerprints and a Residual-Calibrated Ensemble Network

Magnetically coupled wireless power transfer is highly sensitive to the relative height between the transmitter and receiver coils. Meanwhile, complex electromagnetic interference and measurement noise induce fluctuations in the voltage fingerprints and degrade the fingerprint-library quality, which in turn leads to an unstable closed-loop alignment. To address these issues, this study proposes a position identification and closed-loop alignment method based on decoupled sensing-coil voltage fingerprints and integrated neural network regression. First, a four-quadrant symmetric decoupled sensing-coil array is deployed at the transmitter, and a compensation network is introduced to suppress direct-coupling effects. Thus, the multi-channel sensing voltages are primarily driven by the planar position variation of the receiver coil, thereby forming voltage fingerprints with improved spatial separability. Second, an original evaluation metric for centimeter-level alignment reliability is developed, and a lightweight residual calibration strategy is designed to target hard samples and tail errors to alleviate the error amplification caused by fingerprint noise and distribution imbalance. On this basis, a multi-model fusion integrated neural network is constructed to enhance disturbance rejection and prediction consistency. Finally, closed-loop alignment experiments are conducted over a two-dimensional workspace and compared with representative baseline methods. The results demonstrate that the proposed method achieves more concentrated terminal alignment positions and more stable closed-loop behavior, even when the fingerprint library is of low quality, thus validating its engineering applicability and robust alignment capability.