Generating Comprehensive Lithium Battery Charging Data with Generative AI Article Swipe
Lidang Jiang
,
Changyan Hu
,
Sibei Ji
,
Hang Zhao
,
Junxiong Chen
,
Ge He
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.07577
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.07577
In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.07577
- https://arxiv.org/pdf/2404.07577
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394781821
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394781821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.07577Digital Object Identifier
- Title
-
Generating Comprehensive Lithium Battery Charging Data with Generative AIWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-11Full publication date if available
- Authors
-
Lidang Jiang, Changyan Hu, Sibei Ji, Hang Zhao, Junxiong Chen, Ge HeList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.07577Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2404.07577Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2404.07577Direct OA link when available
- Concepts
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Computer science, Battery (electricity), Autoencoder, Inference, Machine learning, Data pre-processing, Artificial intelligence, Data mining, Generative model, Process (computing), Reliability engineering, Deep learning, Generative grammar, Engineering, Power (physics), Physics, Quantum mechanics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lengthy | 55 |
| abstract_inverted_index.lithium | 8, 187, 210 |
| abstract_inverted_index.methods | 19 |
| abstract_inverted_index.models. | 98 |
| abstract_inverted_index.quality | 41 |
| abstract_inverted_index.success | 23 |
| abstract_inverted_index.through | 50 |
| abstract_inverted_index.various | 197 |
| abstract_inverted_index.(RCVAE). | 115 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.accurate | 10 |
| abstract_inverted_index.achieved | 21 |
| abstract_inverted_index.achieves | 125 |
| abstract_inverted_index.charging | 137 |
| abstract_inverted_index.current, | 134 |
| abstract_inverted_index.dataset, | 176 |
| abstract_inverted_index.detailed | 194 |
| abstract_inverted_index.efficacy | 30 |
| abstract_inverted_index.generate | 157 |
| abstract_inverted_index.lifespan | 6 |
| abstract_inverted_index.offering | 204 |
| abstract_inverted_index.pivotal. | 14 |
| abstract_inverted_index.process, | 58 |
| abstract_inverted_index.provides | 170 |
| abstract_inverted_index.research | 180 |
| abstract_inverted_index.specific | 158 |
| abstract_inverted_index.training | 150 |
| abstract_inverted_index.voltage, | 133 |
| abstract_inverted_index.accuracy. | 76 |
| abstract_inverted_index.capacity, | 138 |
| abstract_inverted_index.datasets. | 44 |
| abstract_inverted_index.developed | 109 |
| abstract_inverted_index.embedding | 102 |
| abstract_inverted_index.extending | 4 |
| abstract_inverted_index.including | 132 |
| abstract_inverted_index.inference | 152 |
| abstract_inverted_index.processed | 142 |
| abstract_inverted_index.synthesis | 128, 185 |
| abstract_inverted_index.synthetic | 195 |
| abstract_inverted_index.Addressing | 77 |
| abstract_inverted_index.Equivalent | 89 |
| abstract_inverted_index.approaches | 34 |
| abstract_inverted_index.artificial | 184 |
| abstract_inverted_index.batteries, | 9 |
| abstract_inverted_index.conditions | 94 |
| abstract_inverted_index.customized | 149 |
| abstract_inverted_index.generating | 46 |
| abstract_inverted_index.generative | 96 |
| abstract_inverted_index.indicators | 200 |
| abstract_inverted_index.integrated | 127 |
| abstract_inverted_index.introduces | 82 |
| abstract_inverted_index.optimizing | 1 |
| abstract_inverted_index.pioneering | 177 |
| abstract_inverted_index.prediction | 12, 75 |
| abstract_inverted_index.regression | 16 |
| abstract_inverted_index.supervised | 166 |
| abstract_inverted_index.Autoencoder | 114 |
| abstract_inverted_index.Conditional | 112 |
| abstract_inverted_index.Traditional | 15 |
| abstract_inverted_index.Variational | 113 |
| abstract_inverted_index.algorithms, | 153 |
| abstract_inverted_index.calculated, | 203 |
| abstract_inverted_index.challenges, | 79 |
| abstract_inverted_index.challenging | 61 |
| abstract_inverted_index.conditions. | 167 |
| abstract_inverted_index.data-driven | 33 |
| abstract_inverted_index.difficulty, | 68 |
| abstract_inverted_index.experiments | 52 |
| abstract_inverted_index.integrating | 100 |
| abstract_inverted_index.performance | 2, 212 |
| abstract_inverted_index.prediction. | 27, 213 |
| abstract_inverted_index.quasi-video | 121 |
| abstract_inverted_index.Furthermore, | 190 |
| abstract_inverted_index.availability | 39 |
| abstract_inverted_index.high-quality | 64 |
| abstract_inverted_index.perspectives | 206 |
| abstract_inverted_index.temperature, | 135 |
| abstract_inverted_index.Additionally, | 45 |
| abstract_inverted_index.comprehensive | 174 |
| abstract_inverted_index.possibilities | 208 |
| abstract_inverted_index.predominantly | 49 |
| abstract_inverted_index.preprocessing | 117 |
| abstract_inverted_index.significantly | 73 |
| abstract_inverted_index.classification | 18 |
| abstract_inverted_index.electrochemical | 47, 65, 130, 159, 175 |
| abstract_inverted_index.incompleteness, | 72 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile.value | 0.50787362 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |