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Long-Term Performance Prediction of PEMFC Based on LASSO-ESN
- Source :
- IEEE Transactions on Instrumentation and Measurement. 70:1-11
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- In recent years, with wide application of proton exchange membrane fuel cell (PEMFC) in vehicles and portable applications, researches regarding PEMFC lifetime behavior and associated prognostic techniques receive more interest. In this article, a least absolute shrinkage and selection operator-echo state network (LASSO-ESN)-based prognostic strategy is proposed for the optimization of input parameters and long-term PEMFC behavior prediction. In the analysis, ESN is selected to predict PEMFC long-term behavior iteratively, while input parameters to ESN are optimized using LASSO. With LASSO, the contribution of input parameters to PEMFC prediction can be evaluated, and those with the minimum weight are eliminated iteratively during the prediction. From the findings, the most accurate predictions and corresponding optimized input parameters can be determined. Furthermore, effectiveness of proposed strategy is investigated using PEMFC data at different operating conditions. Results demonstrate that with proposed strategy, optimized input parameters at different operating conditions can be determined, and accurate PEMFC predictions can be provided.
- Subjects :
- Lasso (statistics)
Control theory
Computer science
020208 electrical & electronic engineering
Feature extraction
0202 electrical engineering, electronic engineering, information engineering
Performance prediction
Proton exchange membrane fuel cell
Prognostics
02 engineering and technology
Electrical and Electronic Engineering
Instrumentation
Term (time)
Subjects
Details
- ISSN :
- 15579662 and 00189456
- Volume :
- 70
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Instrumentation and Measurement
- Accession number :
- edsair.doi...........9cd8ff2bf5c25833b4ba5ad2188086e1