Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network Article Swipe
Lidang Jiang
,
Qingsong Huang
,
Ge He
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17071695
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17071695
Accurate Remaining Useful Life (RUL) prediction of lithium batteries is crucial for enhancing their performance and extending their lifespan. Existing studies focus on continuous or relatively sparse datasets; however, continuous and complete datasets are rarely available in practical applications due to missing or inaccessible data. This study attempts to achieve the prediction of lithium battery RUL using random sparse data from only 10 data points, aligning more closely with practical industrial scenarios. Furthermore, we introduce the application of a Flexible Parallel Neural Network (FPNN) for the first time in predicting the RUL of lithium batteries. By combining these two approaches, our tests on the MIT dataset show that by randomly downsampling 10 points per cycle from 10 cycles, we can reconstruct new meaningful features and achieve a Mean Absolute Percentage Error (MAPE) of 2.36% in predicting the RUL. When the input data are limited to the first 10 cycles using the dataset constructed from random downsampling and the FPNN, the predicted RUL MAPE is 0.75%. The method proposed in this study offers an accurate, adaptable, and comprehensible new solution for predicting the RUL of lithium batteries, paving a new research path in the field of battery health monitoring.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en17071695
- https://www.mdpi.com/1996-1073/17/7/1695/pdf?version=1712105565
- OA Status
- gold
- Cited By
- 2
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393866481
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393866481Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en17071695Digital Object Identifier
- Title
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Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-02Full publication date if available
- Authors
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Lidang Jiang, Qingsong Huang, Ge HeList of authors in order
- Landing page
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https://doi.org/10.3390/en17071695Publisher landing page
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https://www.mdpi.com/1996-1073/17/7/1695/pdf?version=1712105565Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1073/17/7/1695/pdf?version=1712105565Direct OA link when available
- Concepts
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Lithium (medication), Artificial neural network, Computer science, Ion, Artificial intelligence, Chemistry, Psychology, Psychiatry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study | 46, 170 |
| abstract_inverted_index.tests | 101 |
| abstract_inverted_index.their | 13, 17 |
| abstract_inverted_index.these | 97 |
| abstract_inverted_index.using | 56, 149 |
| abstract_inverted_index.(FPNN) | 83 |
| abstract_inverted_index.(MAPE) | 131 |
| abstract_inverted_index.0.75%. | 164 |
| abstract_inverted_index.Neural | 81 |
| abstract_inverted_index.Useful | 2 |
| abstract_inverted_index.cycles | 148 |
| abstract_inverted_index.health | 196 |
| abstract_inverted_index.method | 166 |
| abstract_inverted_index.offers | 171 |
| abstract_inverted_index.paving | 186 |
| abstract_inverted_index.points | 112 |
| abstract_inverted_index.random | 57, 154 |
| abstract_inverted_index.rarely | 34 |
| abstract_inverted_index.sparse | 26, 58 |
| abstract_inverted_index.Network | 82 |
| abstract_inverted_index.achieve | 49, 125 |
| abstract_inverted_index.battery | 54, 195 |
| abstract_inverted_index.closely | 67 |
| abstract_inverted_index.crucial | 10 |
| abstract_inverted_index.cycles, | 117 |
| abstract_inverted_index.dataset | 105, 151 |
| abstract_inverted_index.limited | 143 |
| abstract_inverted_index.lithium | 7, 53, 93, 184 |
| abstract_inverted_index.missing | 41 |
| abstract_inverted_index.points, | 64 |
| abstract_inverted_index.studies | 20 |
| abstract_inverted_index.Absolute | 128 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.Existing | 19 |
| abstract_inverted_index.Flexible | 79 |
| abstract_inverted_index.Parallel | 80 |
| abstract_inverted_index.aligning | 65 |
| abstract_inverted_index.attempts | 47 |
| abstract_inverted_index.complete | 31 |
| abstract_inverted_index.datasets | 32 |
| abstract_inverted_index.features | 123 |
| abstract_inverted_index.however, | 28 |
| abstract_inverted_index.proposed | 167 |
| abstract_inverted_index.randomly | 109 |
| abstract_inverted_index.research | 189 |
| abstract_inverted_index.solution | 178 |
| abstract_inverted_index.Remaining | 1 |
| abstract_inverted_index.accurate, | 173 |
| abstract_inverted_index.available | 35 |
| abstract_inverted_index.batteries | 8 |
| abstract_inverted_index.combining | 96 |
| abstract_inverted_index.datasets; | 27 |
| abstract_inverted_index.enhancing | 12 |
| abstract_inverted_index.extending | 16 |
| abstract_inverted_index.introduce | 74 |
| abstract_inverted_index.lifespan. | 18 |
| abstract_inverted_index.practical | 37, 69 |
| abstract_inverted_index.predicted | 160 |
| abstract_inverted_index.Percentage | 129 |
| abstract_inverted_index.adaptable, | 174 |
| abstract_inverted_index.batteries, | 185 |
| abstract_inverted_index.batteries. | 94 |
| abstract_inverted_index.continuous | 23, 29 |
| abstract_inverted_index.industrial | 70 |
| abstract_inverted_index.meaningful | 122 |
| abstract_inverted_index.predicting | 89, 135, 180 |
| abstract_inverted_index.prediction | 5, 51 |
| abstract_inverted_index.relatively | 25 |
| abstract_inverted_index.scenarios. | 71 |
| abstract_inverted_index.application | 76 |
| abstract_inverted_index.approaches, | 99 |
| abstract_inverted_index.constructed | 152 |
| abstract_inverted_index.monitoring. | 197 |
| abstract_inverted_index.performance | 14 |
| abstract_inverted_index.reconstruct | 120 |
| abstract_inverted_index.Furthermore, | 72 |
| abstract_inverted_index.applications | 38 |
| abstract_inverted_index.downsampling | 110, 155 |
| abstract_inverted_index.inaccessible | 43 |
| abstract_inverted_index.comprehensible | 176 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5004977691 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I24185976 |
| citation_normalized_percentile.value | 0.63034102 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |