A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples
Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper prop...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/14/8498 |
| _version_ | 1797590406112739328 |
|---|---|
| author | Haochen Qin Xuexin Fan Yaxiang Fan Ruitian Wang Qianyi Shang Dong Zhang |
| author_facet | Haochen Qin Xuexin Fan Yaxiang Fan Ruitian Wang Qianyi Shang Dong Zhang |
| author_sort | Haochen Qin |
| collection | DOAJ |
| description | Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed. |
| first_indexed | 2024-03-11T01:20:03Z |
| format | Article |
| id | doaj.art-d792a208b3c649e4851473b88d0c8ed4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:03Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d792a208b3c649e4851473b88d0c8ed42023-11-18T18:13:56ZengMDPI AGApplied Sciences2076-34172023-07-011314849810.3390/app13148498A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small SamplesHaochen Qin0Xuexin Fan1Yaxiang Fan2Ruitian Wang3Qianyi Shang4Dong Zhang5National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, ChinaNational Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, ChinaNational Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, ChinaNational Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, ChinaNational Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, ChinaNational Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, ChinaPredicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed.https://www.mdpi.com/2076-3417/13/14/8498lithium-ion batteryremaining useful lifetransfer adaptive boostingCARTbattery management systemedge computing |
| spellingShingle | Haochen Qin Xuexin Fan Yaxiang Fan Ruitian Wang Qianyi Shang Dong Zhang A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples Applied Sciences lithium-ion battery remaining useful life transfer adaptive boosting CART battery management system edge computing |
| title | A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples |
| title_full | A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples |
| title_fullStr | A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples |
| title_full_unstemmed | A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples |
| title_short | A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples |
| title_sort | transferable prediction approach for the remaining useful life of lithium ion batteries based on small samples |
| topic | lithium-ion battery remaining useful life transfer adaptive boosting CART battery management system edge computing |
| url | https://www.mdpi.com/2076-3417/13/14/8498 |
| work_keys_str_mv | AT haochenqin atransferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT xuexinfan atransferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT yaxiangfan atransferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT ruitianwang atransferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT qianyishang atransferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT dongzhang atransferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT haochenqin transferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT xuexinfan transferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT yaxiangfan transferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT ruitianwang transferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT qianyishang transferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples AT dongzhang transferablepredictionapproachfortheremainingusefullifeoflithiumionbatteriesbasedonsmallsamples |