Modeling of DC-DC Converters with Neural Ordinary Differential Equations

Nowadays, the data-driven approaches to modeling power electronic systems are mostly based on common neural network (NN) structures without any prior knowledge of physics. However, these approaches often demand extensive data across a wide range of inputs and may encounter overfitting issues. To address this issue, this paper proposes a new data-driven modeling approach for DC-DC converters based on neural ordinary differential equations (ODE). This approach incorporates prior knowledge about numerical ODE solvers into the NN structures, leading to a substantial improvement in generalization performance. In this method, the kernel function of numerical integration is replaced by a fully-connected NN, and the system ODE is solved through common numerical solvers iteratively in time. The results of our study demonstrate a significant enhancement in generalization performance across various input frequencies using variable step solvers while maintaining excellent precision.