Recognizing Multitype Misalignments in Wireless EV Chargers With Orientation-Sensitive Coils: A Data-Driven Strategy Using Improved ResNet

Abstract

In wireless electric vehicle charging systems, misalignments between ground-assembled transmitter and vehicle-assembled receiver are undesirable but inevitable, which may cause efficiency deterioration and even thermal risks. The recognition of horizontal misalignment, including that in the longitudinal and lateral direction, has been made gradual progress by previous works. While yaw angle, with rare attention, can also degrade the charging performance, especially for those employing non-centrosymmetric coupling coils, such as rectangular coils. In this paper, a data-driven strategy based on improved ResNet is proposed to recognize the above multi-type misalignments simultaneously, which is accurate, reliable, and of practical and general value. Large-scale data is sampled using the proposed detection coils by double-group cooperation, which features high sensitivity to the misalignments of all kinds, especially yaw angle. Benefit to the compression technique in the pre-processing phase, the number of training data and labels can be successfully reduced by 3/4. As the core algorithm of the overall strategy, the adaptive channel parameter recalibration ResNet (ACPR-ResNet) can effectively perceive slight differences among similar input samples and map them to proper labels. This is achieved by an adaptive operation of nonlinear transformation and recalibrated importance of channel features during the training process. The effectiveness of the proposed strategy is verified experimentally using a 6.6-kW prototype. Within the 24×24 cm range, 95.2% of the test cases have an error of less than 1.7 cm when recognizing horizontal misalignment, and 91.7% of the test cases have an error of less than 1.5° when recognizing yaw angle.