Data-Driven Prediction of Induced Voltage in CT-Based Magnetic Energy Harvesting Systems Considering Nonlinear B-H...

This paper presents a study on the development of a deep neural network (DNN)-based surrogate model for rapid induced voltage prediction in current transformer (CT)-based magnetic energy harvesting systems. CT-based magnetic energy harvesters are promising self-powered energy sources for low-power electronic devices, but their output performance is strongly affected by the nonlinear magnetic behavior of the core material. Therefore, the accurate prediction of the induced voltage is important for device design. However, evaluating the voltage response under various magnetic material characteristics and operating conditions through repeated electromagnetic simulations requires considerable computational effort. In this study, nonlinear B-H curves were parameterized using an arctangent-based model, and electromagnetic simulations were performed by varying the magnetic material parameters, primary current, and load resistance. The resulting dataset was used to train and validate the DNN surrogate model. The trained model showed high prediction accuracy, with an R² value greater than 0.99 and low prediction errors. It also reproduced the RMS induced voltage trends for different magnetic material characteristics and operating conditions and was further used for maximum power point analysis within the investigated parameter range. These results indicate that the proposed surrogate model can reduce the need for repeated electromagnetic simulations and support the efficient design exploration of CT-based magnetic energy harvesting systems.