Physics Informed Neural Network Estimated Circuit Parameter Adaptive Modulation of DAB

TL;DR

This paper introduces a physics-informed neural network that estimates circuit parameters to adaptively optimize the modulation scheme of a dual-active-bridge DC-DC converter, enhancing performance and robustness.

Contribution

It presents a novel method combining physics-informed neural networks with adaptive modulation for improved circuit control.

Findings

Effective circuit parameter estimation using neural networks

Enhanced modulation scheme performance through adaptive control

Validated approach improves converter efficiency and stability

Abstract

This article presents the development, implementation, and validation of a loss-optimized and circuit parameter-sensitive TPS modulation scheme for a dual-active-bridge DC-DC converter. The proposed approach dynamically adjusts control parameters based on circuit parameters estimated using a physics-informed neural network.