A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM
Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFC...
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| Vydané v: | Sensors (Basel, Switzerland) Ročník 23; číslo 1; s. 166 |
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24.12.2022
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| Abstract | Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method. |
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| AbstractList | Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method. Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method.Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method. |
| Audience | Academic |
| Author | Xia, Zetao Tian, Guanzhong Li, Yongjie Tao, Jili Wang, Yining Ma, Longhua Zhu, Yang |
| AuthorAffiliation | 2 School of Information Science and Engineering, NingboTech University, Ningbo 315000, China 1 Ningbo Innovation Center, Zhejiang University, Ningbo 315000, China 3 College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China |
| AuthorAffiliation_xml | – name: 1 Ningbo Innovation Center, Zhejiang University, Ningbo 315000, China – name: 2 School of Information Science and Engineering, NingboTech University, Ningbo 315000, China – name: 3 College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China |
| Author_xml | – sequence: 1 givenname: Zetao orcidid: 0000-0002-4851-0224 surname: Xia fullname: Xia, Zetao – sequence: 2 givenname: Yining surname: Wang fullname: Wang, Yining – sequence: 3 givenname: Longhua surname: Ma fullname: Ma, Longhua – sequence: 4 givenname: Yang surname: Zhu fullname: Zhu, Yang – sequence: 5 givenname: Yongjie surname: Li fullname: Li, Yongjie – sequence: 6 givenname: Jili surname: Tao fullname: Tao, Jili – sequence: 7 givenname: Guanzhong orcidid: 0000-0002-7292-4056 surname: Tian fullname: Tian, Guanzhong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36616764$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Accuracy Aging Algorithms Decomposition degradation prediction Forecasting Fuel cell industry Fuel cells hybrid method Investigations Kalman filters Loess Machine learning Methods Neural networks Power electronics prognostics proton-exchange-membrane fuel cell remaining useful life Trends |
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| Title | A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM |
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