Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data
This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Ex...
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MDPI AG
2024-11-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/10/11/389 |
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| author | Yong Seok Bae Sungwon Lee Janghyuk Moon |
| author_facet | Yong Seok Bae Sungwon Lee Janghyuk Moon |
| author_sort | Yong Seok Bae |
| collection | DOAJ |
| description | This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage. |
| format | Article |
| id | doaj-art-178ba371e8c94dc0b8a19edfbebf2e0e |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-178ba371e8c94dc0b8a19edfbebf2e0e2025-08-20T02:08:02ZengMDPI AGBatteries2313-01052024-11-01101138910.3390/batteries10110389Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation DataYong Seok Bae0Sungwon Lee1Janghyuk Moon2Department of Energy Systems Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Energy Systems Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Energy Systems Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaThis study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage.https://www.mdpi.com/2313-0105/10/11/389lithium-ion batteryremaining useful life (RUL)sequence-to-sequence (Seq2Seq)acceleration of battery degradation |
| spellingShingle | Yong Seok Bae Sungwon Lee Janghyuk Moon Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data Batteries lithium-ion battery remaining useful life (RUL) sequence-to-sequence (Seq2Seq) acceleration of battery degradation |
| title | Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data |
| title_full | Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data |
| title_fullStr | Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data |
| title_full_unstemmed | Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data |
| title_short | Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data |
| title_sort | developing an innovative seq2seq model to predict the remaining useful life of low charged battery performance using high speed degradation data |
| topic | lithium-ion battery remaining useful life (RUL) sequence-to-sequence (Seq2Seq) acceleration of battery degradation |
| url | https://www.mdpi.com/2313-0105/10/11/389 |
| work_keys_str_mv | AT yongseokbae developinganinnovativeseq2seqmodeltopredicttheremainingusefullifeoflowchargedbatteryperformanceusinghighspeeddegradationdata AT sungwonlee developinganinnovativeseq2seqmodeltopredicttheremainingusefullifeoflowchargedbatteryperformanceusinghighspeeddegradationdata AT janghyukmoon developinganinnovativeseq2seqmodeltopredicttheremainingusefullifeoflowchargedbatteryperformanceusinghighspeeddegradationdata |