Fuel cell parameter identification method and apparatus, and device and storage medium

Through the multi-strategy sparrow search optimization algorithm combined with Tent chaotic mapping and elite population strategy, the low search efficiency and easy to fall into the local optimal solution problem in the parameter identification of proton exchange membrane fuel cell is solved, achieving more efficient and accurate parameter identification.

WO2025162502A1 Publication Date: 2025-08-07XI AN JIAOTONG UNIV
5 Cites 13 Cited by

Patent Information

Application Number
PCT/CN2025/081793
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-04-30
Filing Date
2025-03-11
Publication Date
2025-08-07

AI Technical Summary

Technical Problem

The existing sparrow search optimization algorithm has problems such as low search efficiency and easy to fall into local optimal solutions in the parameter identification of proton exchange membrane fuel cell, and it is difficult to accurately and efficiently identify the parameters of the semi-empirical model.

Method used

The multi-strategy sparrow search optimization algorithm is adopted, combined with Tent chaotic mapping and elite population strategy, and the parameter identification process of proton exchange membrane fuel cell is optimized through Levi flight perturbation and DE/best/1 mutation strategy, and a semi-empirical model is constructed and mean square error is minimized.

Benefits of technology

It improves the accuracy and efficiency of parameter identification, reduces the risk of convergence precocious puberty, enhances the global and local optimization ability, and obtains better identification results.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention relates to the technical field of proton exchange membrane fuel cells. Disclosed are a fuel cell parameter identification method and apparatus, and a device and a storage medium. The method comprises the following steps: constructing a semi-empirical model for a proton exchange membrane fuel cell; calculating a theoretical value of an output voltage of the semi-empirical model, and constructing an objective function by means of a mean square error between the theoretical value and an actual value of the output voltage of the semi-empirical model; on the basis of the semi-empirical model, determining a plurality of parameters to be identified, and using said plurality of parameters as decision variables to construct a plurality of constraint conditions for the objective function; on the basis of the plurality of constraint conditions, constructing an optimization model for the proton exchange membrane fuel cell by using the minimization of the objective function as an optimization objective and using said plurality of parameters as variables to be solved; and solving the optimization model by means of a multi-policy sparrow search optimization algorithm, so as to obtain a plurality of optimal parameters to be identified. In the multi-policy sparrow search optimization algorithm in the present invention, Tent chaotic mapping is introduced to initialize a population, the number of populations is increased, and then the two populations are merged. An adaptive feedback mechanism is added in a follower position update stage and a vigilant position update stage, thereby reducing the convergence accuracy under a limited number of iterations. A DE / best / 1 mutation policy and a dynamic scaling factor sf are used to update the position of a sparrow. The global optimization capability and the local optimization capability of an algorithm are improved, thereby also improving the accuracy of fuel cell parameter identification.
Need to check novelty before this filing date? Find Prior Art