A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries
Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/3838765 |
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| author | Wen-An Yang Maohua Xiao Wei Zhou Yu Guo Wenhe Liao |
| author_facet | Wen-An Yang Maohua Xiao Wei Zhou Yu Guo Wenhe Liao |
| author_sort | Wen-An Yang |
| collection | DOAJ |
| description | Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach. |
| format | Article |
| id | doaj-art-6fedae5e85d14f63814125a5ca2a46e7 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6fedae5e85d14f63814125a5ca2a46e72025-02-03T06:13:44ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/38387653838765A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion BatteriesWen-An Yang0Maohua Xiao1Wei Zhou2Yu Guo3Wenhe Liao4College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaNanjing Surveying and Mapping Instrument Factory, Nanjing 210003, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaLithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.http://dx.doi.org/10.1155/2016/3838765 |
| spellingShingle | Wen-An Yang Maohua Xiao Wei Zhou Yu Guo Wenhe Liao A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries Shock and Vibration |
| title | A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries |
| title_full | A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries |
| title_fullStr | A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries |
| title_full_unstemmed | A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries |
| title_short | A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries |
| title_sort | hybrid prognostic approach for remaining useful life prediction of lithium ion batteries |
| url | http://dx.doi.org/10.1155/2016/3838765 |
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