A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespan Article Swipe
Lidang Jiang
,
Zhuoxiang Li
,
Changyan Hu
,
Qingsong Huang
,
Ge He
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.energy.2024.132840
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.energy.2024.132840
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.energy.2024.132840
- OA Status
- green
- Cited By
- 3
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391376780
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391376780Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.energy.2024.132840Digital Object Identifier
- Title
-
A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespanWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-22Full publication date if available
- Authors
-
Lidang Jiang, Zhuoxiang Li, Changyan Hu, Qingsong Huang, Ge HeList of authors in order
- Landing page
-
https://doi.org/10.1016/j.energy.2024.132840Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.16102Direct OA link when available
- Concepts
-
Overfitting, Interpretability, Computer science, Artificial intelligence, Convolutional neural network, Artificial neural network, Deep learning, Abstraction, Machine learning, Battery (electricity), Segmentation, Philosophy, Epistemology, Quantum mechanics, Power (physics), PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4391376780 |
|---|---|
| doi | https://doi.org/10.1016/j.energy.2024.132840 |
| ids.doi | https://doi.org/10.1016/j.energy.2024.132840 |
| ids.openalex | https://openalex.org/W4391376780 |
| fwci | 1.19787404 |
| type | preprint |
| title | A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespan |
| biblio.issue | |
| biblio.volume | 308 |
| biblio.last_page | 132840 |
| biblio.first_page | 132840 |
| topics[0].id | https://openalex.org/T10663 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2203 |
| topics[0].subfield.display_name | Automotive Engineering |
| topics[0].display_name | Advanced Battery Technologies Research |
| topics[1].id | https://openalex.org/T10018 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9922000169754028 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Advancements in Battery Materials |
| topics[2].id | https://openalex.org/T10281 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9771999716758728 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Advanced Battery Materials and Technologies |
| is_xpac | False |
| apc_list.value | 3980 |
| apc_list.currency | USD |
| apc_list.value_usd | 3980 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C22019652 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8785101175308228 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q331309 |
| concepts[0].display_name | Overfitting |
| concepts[1].id | https://openalex.org/C2781067378 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8115472793579102 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q17027399 |
| concepts[1].display_name | Interpretability |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7548753023147583 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6593417525291443 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C81363708 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5636209845542908 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[4].display_name | Convolutional neural network |
| concepts[5].id | https://openalex.org/C50644808 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5433263778686523 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[5].display_name | Artificial neural network |
| concepts[6].id | https://openalex.org/C108583219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5197746157646179 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[6].display_name | Deep learning |
| concepts[7].id | https://openalex.org/C124304363 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5187892317771912 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q673661 |
| concepts[7].display_name | Abstraction |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.475938081741333 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C555008776 |
| concepts[9].level | 3 |
| concepts[9].score | 0.46575552225112915 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q267298 |
| concepts[9].display_name | Battery (electricity) |
| concepts[10].id | https://openalex.org/C89600930 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4157012701034546 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[10].display_name | Segmentation |
| concepts[11].id | https://openalex.org/C138885662 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[11].display_name | Philosophy |
| concepts[12].id | https://openalex.org/C111472728 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[12].display_name | Epistemology |
| concepts[13].id | https://openalex.org/C62520636 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[13].display_name | Quantum mechanics |
| concepts[14].id | https://openalex.org/C163258240 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[14].display_name | Power (physics) |
| concepts[15].id | https://openalex.org/C121332964 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[15].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/overfitting |
| keywords[0].score | 0.8785101175308228 |
| keywords[0].display_name | Overfitting |
| keywords[1].id | https://openalex.org/keywords/interpretability |
| keywords[1].score | 0.8115472793579102 |
| keywords[1].display_name | Interpretability |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7548753023147583 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6593417525291443 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[4].score | 0.5636209845542908 |
| keywords[4].display_name | Convolutional neural network |
| keywords[5].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[5].score | 0.5433263778686523 |
| keywords[5].display_name | Artificial neural network |
| keywords[6].id | https://openalex.org/keywords/deep-learning |
| keywords[6].score | 0.5197746157646179 |
| keywords[6].display_name | Deep learning |
| keywords[7].id | https://openalex.org/keywords/abstraction |
| keywords[7].score | 0.5187892317771912 |
| keywords[7].display_name | Abstraction |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.475938081741333 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/battery |
| keywords[9].score | 0.46575552225112915 |
| keywords[9].display_name | Battery (electricity) |
| keywords[10].id | https://openalex.org/keywords/segmentation |
| keywords[10].score | 0.4157012701034546 |
| keywords[10].display_name | Segmentation |
| language | en |
| locations[0].id | doi:10.1016/j.energy.2024.132840 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S2497375143 |
| locations[0].source.issn | 0360-5442, 1873-6785 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0360-5442 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Energy |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Energy |
| locations[0].landing_page_url | https://doi.org/10.1016/j.energy.2024.132840 |
| locations[1].id | pmh:oai:arXiv.org:2401.16102 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/2401.16102 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/2401.16102 |
| indexed_in | arxiv, crossref |
| authorships[0].author.id | https://openalex.org/A5102611306 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Lidang Jiang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiang, Lidang |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5028199072 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7789-6341 |
| authorships[1].author.display_name | Zhuoxiang Li |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Li, Zhuoxiang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5078324301 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Changyan Hu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hu, Changyan |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5018442454 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1878-3579 |
| authorships[3].author.display_name | Qingsong Huang |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Huang, Qingsong |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5101040586 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Ge He |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | He, Ge |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2401.16102 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-01-31T00:00:00 |
| display_name | A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespan |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10663 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2203 |
| primary_topic.subfield.display_name | Automotive Engineering |
| primary_topic.display_name | Advanced Battery Technologies Research |
| related_works | https://openalex.org/W4362597605, https://openalex.org/W1574414179, https://openalex.org/W2905433371, https://openalex.org/W4297676672, https://openalex.org/W3009056573, https://openalex.org/W2922073769, https://openalex.org/W4281702477, https://openalex.org/W2888392564, https://openalex.org/W4310278675, https://openalex.org/W4388422664 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2401.16102 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2401.16102 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2401.16102 |
| primary_location.id | doi:10.1016/j.energy.2024.132840 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S2497375143 |
| primary_location.source.issn | 0360-5442, 1873-6785 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0360-5442 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Energy |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Energy |
| primary_location.landing_page_url | https://doi.org/10.1016/j.energy.2024.132840 |
| publication_date | 2024-08-22 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2125467115, https://openalex.org/W2972878444, https://openalex.org/W3165236143, https://openalex.org/W1985031009, https://openalex.org/W2796854462, https://openalex.org/W1985140644, https://openalex.org/W3036910660, https://openalex.org/W3174980679, https://openalex.org/W150714319, https://openalex.org/W3111760388, https://openalex.org/W3081455800, https://openalex.org/W3080742610, https://openalex.org/W2898485094, https://openalex.org/W2924382816, https://openalex.org/W2333401465, https://openalex.org/W1901616594, https://openalex.org/W6754019226, https://openalex.org/W2080562691, https://openalex.org/W1964940342, https://openalex.org/W6680298704, https://openalex.org/W2980831607, https://openalex.org/W3010408268, https://openalex.org/W3062552330, https://openalex.org/W3135514141, https://openalex.org/W2919115771, https://openalex.org/W3125035928, https://openalex.org/W3027617872, https://openalex.org/W3018139049, https://openalex.org/W3040694753, https://openalex.org/W3093838722, https://openalex.org/W4360852128, https://openalex.org/W6739901393, https://openalex.org/W6854598472, https://openalex.org/W4205522342, https://openalex.org/W4307057868, https://openalex.org/W3213408856, https://openalex.org/W3119025527, https://openalex.org/W3155363590, https://openalex.org/W2964350391, https://openalex.org/W1983364832, https://openalex.org/W6682864246, https://openalex.org/W3216627126, https://openalex.org/W6678911119, https://openalex.org/W4387683771 |
| referenced_works_count | 44 |
| abstract_inverted_index | |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.69816951 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |