基于网络化深度迁移学习的无通信感应式无线充电恒流/恒压控制

Parameter identification-based control strategies are considered the preferred solution for achieving constant current (CC) and constant voltage (CV) charging control in communication-free inductive power transfer systems. However, identification errors in mutual inductance, load resistance, and other parameters can affect control accuracy. To improve control accuracy and response speed, a communication-free control strategy based on neural networks and deep transfer learning is proposed. This strategy eliminates the need to identify parameters like mutual inductance and load resistance. Only a few measured datasets are required to train the network model offline, and a trained model can online estimate output voltage/current. By combining with a controller, CV/CC charging control can be achieved under conditions of real-time variations in mutual inductance and load resistance. The experimental results show that the proposed control strategy achieves a static error of only 1.5% and a response time of no more than 24 ms. Compared to the parameter identification-based control strategy, the proposed strategy demonstrates lower static error, shorter response time, and a wider dynamic range.