Method and system for diagnosing and estimating health state of fuel cell based on polarization characteristics
By constructing multi-condition multi-physics model and feature fusion classification technology, the existing fuel cell health status diagnosis methods rely on expert experience and high implementation costs are solved, and high-precision and low-cost health status diagnosis is achieved.
CN119674142AActive Publication Date: 2025-03-21WUHAN UNIV OF TECH
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Patent Information
- Application Number
- CN202411954134.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-03-21
- Estimated Expiration
- 2044-12-27
AI Technical Summary
Technical Problem
The existing fuel cell health status diagnosis methods rely on expert experience, are costly to implement, and it is difficult to extract health status characteristics with high information dimensions.
Method used
By constructing a multi-condition multi-physics model, the health status characteristics of the proton exchange membrane fuel cell are extracted, and the characteristics are fusion and classification are performed using technologies such as least squares support vector machine and radial basis function to obtain the fuel cell health status diagnostic model.
Benefits of technology
It realizes high-precision fuel cell health status diagnosis, reduces dependence on expert experience, is low in cost, and has strong interpretability.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides a method and a system for diagnosing and estimating the state of health of a fuel cell based on polarization characteristics, and relates to the technical field of fuel cells, and the method comprises the following steps: constructing a proton exchange membrane fuel cell model based on a multi-condition multi-physical field, extracting health state characteristics of the to-be-diagnosed battery from the proton exchange membrane fuel cell model under a preset typical working condition; and inputting the health state characteristics into a trained fuel cell health state diagnosis model to obtain the health state of the to-be-diagnosed battery evaluated by the fuel cell health state diagnosis model. The method not only can accurately diagnose the health state of the fuel cell, but also is low in implementation difficulty and cost and relatively high in interpretability, so that the dependence of high-precision diagnosis of the health state of the fuel cell on expert experience is reduced.
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