Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis
The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/9/2371 |
| _version_ | 1797536749378863104 |
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| author | Matthieu Dubarry David Beck |
| author_facet | Matthieu Dubarry David Beck |
| author_sort | Matthieu Dubarry |
| collection | DOAJ |
| description | The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO<sub>4</sub>, Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters. |
| first_indexed | 2024-03-10T12:05:12Z |
| format | Article |
| id | doaj.art-e8c9d81fdb27412ca586abb9545c966a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:05:12Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e8c9d81fdb27412ca586abb9545c966a2023-11-21T16:41:15ZengMDPI AGEnergies1996-10732021-04-01149237110.3390/en14092371Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and PrognosisMatthieu Dubarry0David Beck1Hawaii Natural Energy Institute, University of Hawai̒i at Mānoa, Honolulu, HI 96822, USAHawaii Natural Energy Institute, University of Hawai̒i at Mānoa, Honolulu, HI 96822, USAThe development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO<sub>4</sub>, Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters.https://www.mdpi.com/1996-1073/14/9/2371V vs. Q curvessynthetic dataLFPNCANMC 811BDG tier 1 challenge |
| spellingShingle | Matthieu Dubarry David Beck Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis Energies V vs. Q curves synthetic data LFP NCA NMC 811 BDG tier 1 challenge |
| title | Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis |
| title_full | Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis |
| title_fullStr | Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis |
| title_full_unstemmed | Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis |
| title_short | Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis |
| title_sort | analysis of synthetic voltage vs capacity datasets for big data li ion diagnosis and prognosis |
| topic | V vs. Q curves synthetic data LFP NCA NMC 811 BDG tier 1 challenge |
| url | https://www.mdpi.com/1996-1073/14/9/2371 |
| work_keys_str_mv | AT matthieudubarry analysisofsyntheticvoltagevscapacitydatasetsforbigdataliiondiagnosisandprognosis AT davidbeck analysisofsyntheticvoltagevscapacitydatasetsforbigdataliiondiagnosisandprognosis |