Parameters Identification of Fluxgate Magnetic Core Adopting the Biogeography-Based Optimization Algorithm

Area of Science:

• Materials Science
• Electrical Engineering
• Computational Physics

Background:

• Magnetic fluxgate sensor performance relies heavily on the hysteresis characteristics of its magnetic core.
• Accurate modeling of core hysteresis is essential for reliable sensor design and simulation.
• The Jiles-Atherton model precisely captures ferromagnetic material hysteresis but faces parameter sensitivity challenges.

Purpose of the Study:

• To accurately identify Jiles-Atherton model parameters for magnetic cores.
• To enhance the performance of the Biogeography-Based Optimization (BBO) algorithm for parameter identification.
• To validate the effectiveness of the proposed improved algorithm in fluxgate sensor modeling.

Main Methods:

• Development of an improved Biogeography-Based Optimization (IBBO) algorithm incorporating Arnold map and Differential Evolution (DE) mutation.
• Application of the IBBO algorithm to identify Jiles-Atherton model parameters for permalloy.
• Comparison of IBBO performance against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and BBO.

Main Results:

• The IBBO algorithm demonstrated superior identification accuracy and convergence rate compared to GA, PSO, DE, and BBO.
• Simulated hysteresis loops using IBBO-identified parameters showed high agreement with experimental data for permalloy.
• Fluxgate probe simulations using permalloy cores with IBBO-identified parameters were consistent with experimental outputs.

Conclusions:

• The IBBO algorithm accurately identifies Jiles-Atherton model parameters, overcoming practical challenges.
• This precise parameter identification provides a foundation for advanced analysis and design of magnetic core-based instruments.
• The study validates the IBBO algorithm's efficacy for accurate ferromagnetic material modeling in sensor applications.