Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction
Remaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a co...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/8/10/192 |
| _version_ | 1797475248950476800 |
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| author | Sebastian Matthias Hell Chong Dae Kim |
| author_facet | Sebastian Matthias Hell Chong Dae Kim |
| author_sort | Sebastian Matthias Hell |
| collection | DOAJ |
| description | Remaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a combination of gated recurrent unit neural network (GRU NN) and soft-sensing method. Firstly, an indirect health indicator (HI) was extracted from the charging processes using a soft-sensing method that can accurately describe power degradation instead of capacity. Then, a GRU NN with a sliding window was applied to learn the long-term performance development. The method also uses a dropout and early stopping method to prevent overfitting. To build the models and validate the effectiveness of the proposed method, a real-world NASA battery data set with various battery measurements was used. The results show that the method can produce a long-term and accurate RUL prediction at each position of the degradation progression based on several historical battery data sets. |
| first_indexed | 2024-03-09T20:41:26Z |
| format | Article |
| id | doaj.art-07dbb844125f493a9bc4ef3fee56327d |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-09T20:41:26Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-07dbb844125f493a9bc4ef3fee56327d2023-11-23T22:55:38ZengMDPI AGBatteries2313-01052022-10-0181019210.3390/batteries8100192Development of a Data-Driven Method for Online Battery Remaining-Useful-Life PredictionSebastian Matthias Hell0Chong Dae Kim1Technische Hochschule Köln, 50679 Köln, GermanyTechnische Hochschule Köln, 50679 Köln, GermanyRemaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a combination of gated recurrent unit neural network (GRU NN) and soft-sensing method. Firstly, an indirect health indicator (HI) was extracted from the charging processes using a soft-sensing method that can accurately describe power degradation instead of capacity. Then, a GRU NN with a sliding window was applied to learn the long-term performance development. The method also uses a dropout and early stopping method to prevent overfitting. To build the models and validate the effectiveness of the proposed method, a real-world NASA battery data set with various battery measurements was used. The results show that the method can produce a long-term and accurate RUL prediction at each position of the degradation progression based on several historical battery data sets.https://www.mdpi.com/2313-0105/8/10/192lithium-ion batteriesremaining-useful-life (RUL)gated recurrent unit neural network (GRU NN)real-world data |
| spellingShingle | Sebastian Matthias Hell Chong Dae Kim Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction Batteries lithium-ion batteries remaining-useful-life (RUL) gated recurrent unit neural network (GRU NN) real-world data |
| title | Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction |
| title_full | Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction |
| title_fullStr | Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction |
| title_full_unstemmed | Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction |
| title_short | Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction |
| title_sort | development of a data driven method for online battery remaining useful life prediction |
| topic | lithium-ion batteries remaining-useful-life (RUL) gated recurrent unit neural network (GRU NN) real-world data |
| url | https://www.mdpi.com/2313-0105/8/10/192 |
| work_keys_str_mv | AT sebastianmatthiashell developmentofadatadrivenmethodforonlinebatteryremainingusefullifeprediction AT chongdaekim developmentofadatadrivenmethodforonlinebatteryremainingusefullifeprediction |