Work

Gradient Descent‐Based Local Equivalent Resistivity Identification for Fuel Cell Diagnosis by Magnetic Tomography

Year: 2025

Type: article

Abstract: ABSTRACT This work presents a new inverse algorithm based on magnetic tomography (MT) for diagnosing fuel‐cell stacks (FC‐stacks). The objective is to determine the local internal resistivities of the stack from external magnetic measurements. The inverse problem is solved by minimizing the difference between the simulated magnetic induction and the measured magnetic induction using the gradient descent method. To limit the search space, a neighbor‐regularization is implemented as a penalty term, and the stack voltage is added as a constraint. To accelerate the solving process and improve the accuracy, we provide the derivatives of our optimization problem. This involves calculating the derivative of the employed finite element method (FEM) model, where sensitivities are determined using the adjoint‐state method. To validate our novel approach, the algorithm is applied to numerical data and subsequently tested it on measurements from a real FC‐stack, presenting the results in this article.

Source: International Journal for Numerical Methods in Engineering

DOI: 10.1002/nme.70168

Authors: Leonard Freisem, Olivier Chadebec, Gilles Cauffet, Sebastien Rosini, Yann Bultel