Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles Article Swipe
Marat Sadykov
,
Sam Haines
,
Mark A. H. Broadmeadow
,
Geoffrey R. Walker
,
David W. Holmes
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16041628
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16041628
This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources as possible on openly available battery data. The results highlighted an average percentage accuracy of 96.56% for the correct SoC estimation and several drawbacks of the overall implementation, and we propose potential solutions for further improvement. Every implemented model had a similar drawback, which was the poor capturing of the middle area of charge, applying a higher weight to the voltage than the current. The combination of these techniques into a single custom model could result in a better-suited model, further improving the accuracy.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en16041628
- https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520
- OA Status
- gold
- Cited By
- 8
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319442002
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4319442002Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en16041628Digital Object Identifier
- Title
-
Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric VehiclesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-06Full publication date if available
- Authors
-
Marat Sadykov, Sam Haines, Mark A. H. Broadmeadow, Geoffrey R. Walker, David W. HolmesList of authors in order
- Landing page
-
https://doi.org/10.3390/en16041628Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520Direct OA link when available
- Concepts
-
State of charge, Computer science, Battery (electricity), Machine learning, Artificial intelligence, Usability, Simulation, Power (physics), Quantum mechanics, Physics, Human–computer interactionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4319442002 |
|---|---|
| doi | https://doi.org/10.3390/en16041628 |
| ids.doi | https://doi.org/10.3390/en16041628 |
| ids.openalex | https://openalex.org/W4319442002 |
| fwci | 1.30848959 |
| type | article |
| title | Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles |
| biblio.issue | 4 |
| biblio.volume | 16 |
| biblio.last_page | 1628 |
| biblio.first_page | 1628 |
| 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 | 1.0 |
| 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/T10768 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9951000213623047 |
| 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 | Electric Vehicles and Infrastructure |
| topics[2].id | https://openalex.org/T10018 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9929999709129333 |
| 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 | Advancements in Battery Materials |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C2776582896 |
| concepts[0].level | 4 |
| concepts[0].score | 0.7257981896400452 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5368536 |
| concepts[0].display_name | State of charge |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5759896039962769 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C555008776 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5638932585716248 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q267298 |
| concepts[2].display_name | Battery (electricity) |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.49124759435653687 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4771972596645355 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C170130773 |
| concepts[5].level | 2 |
| concepts[5].score | 0.44090813398361206 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q216378 |
| concepts[5].display_name | Usability |
| concepts[6].id | https://openalex.org/C44154836 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3360324800014496 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q45045 |
| concepts[6].display_name | Simulation |
| concepts[7].id | https://openalex.org/C163258240 |
| concepts[7].level | 2 |
| concepts[7].score | 0.15774065256118774 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[7].display_name | Power (physics) |
| concepts[8].id | https://openalex.org/C62520636 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[8].display_name | Quantum mechanics |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C107457646 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q207434 |
| concepts[10].display_name | Human–computer interaction |
| keywords[0].id | https://openalex.org/keywords/state-of-charge |
| keywords[0].score | 0.7257981896400452 |
| keywords[0].display_name | State of charge |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5759896039962769 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/battery |
| keywords[2].score | 0.5638932585716248 |
| keywords[2].display_name | Battery (electricity) |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.49124759435653687 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.4771972596645355 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/usability |
| keywords[5].score | 0.44090813398361206 |
| keywords[5].display_name | Usability |
| keywords[6].id | https://openalex.org/keywords/simulation |
| keywords[6].score | 0.3360324800014496 |
| keywords[6].display_name | Simulation |
| keywords[7].id | https://openalex.org/keywords/power |
| keywords[7].score | 0.15774065256118774 |
| keywords[7].display_name | Power (physics) |
| language | en |
| locations[0].id | doi:10.3390/en16041628 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S198098182 |
| locations[0].source.issn | 1996-1073 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1996-1073 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Energies |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Energies |
| locations[0].landing_page_url | https://doi.org/10.3390/en16041628 |
| locations[1].id | pmh:oai:doaj.org/article:5014b211dd404cd1948b7cd4558dd267 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Energies, Vol 16, Iss 4, p 1628 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/5014b211dd404cd1948b7cd4558dd267 |
| locations[2].id | pmh:oai:mdpi.com:/1996-1073/16/4/1628/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Energies |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/en16041628 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5007684940 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6436-7069 |
| authorships[0].author.display_name | Marat Sadykov |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[0].institutions[0].id | https://openalex.org/I160993911 |
| authorships[0].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Queensland University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Marat Sadykov |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[1].author.id | https://openalex.org/A5065613758 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Sam Haines |
| authorships[1].countries | AU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[1].institutions[0].id | https://openalex.org/I160993911 |
| authorships[1].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[1].institutions[0].country_code | AU |
| authorships[1].institutions[0].display_name | Queensland University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sam Haines |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[2].author.id | https://openalex.org/A5037723484 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7041-5904 |
| authorships[2].author.display_name | Mark A. H. Broadmeadow |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Electrical Engineering & Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[2].institutions[0].id | https://openalex.org/I160993911 |
| authorships[2].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | Queensland University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mark Broadmeadow |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Electrical Engineering & Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[3].author.id | https://openalex.org/A5072200978 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8137-9507 |
| authorships[3].author.display_name | Geoffrey R. Walker |
| authorships[3].countries | AU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Electrical Engineering & Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[3].institutions[0].id | https://openalex.org/I160993911 |
| authorships[3].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | Queensland University of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Geoff Walker |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Electrical Engineering & Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[4].author.id | https://openalex.org/A5045671998 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2970-9158 |
| authorships[4].author.display_name | David W. Holmes |
| authorships[4].countries | AU |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| authorships[4].institutions[0].id | https://openalex.org/I160993911 |
| authorships[4].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[4].institutions[0].country_code | AU |
| authorships[4].institutions[0].display_name | Queensland University of Technology |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | David William Holmes |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | School of Mechanical, Medical and Process Engineering (MMPE), Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles |
| has_fulltext | True |
| 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 | 1.0 |
| 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/W1562159987, https://openalex.org/W2004734718, https://openalex.org/W3190332208, https://openalex.org/W4388623464, https://openalex.org/W3126531014, https://openalex.org/W2519883542, https://openalex.org/W2098124661, https://openalex.org/W3006423619, https://openalex.org/W4385625847, https://openalex.org/W3195056770 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/en16041628 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S198098182 |
| best_oa_location.source.issn | 1996-1073 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1996-1073 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Energies |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Energies |
| best_oa_location.landing_page_url | https://doi.org/10.3390/en16041628 |
| primary_location.id | doi:10.3390/en16041628 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S198098182 |
| primary_location.source.issn | 1996-1073 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1996-1073 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Energies |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1996-1073/16/4/1628/pdf?version=1676622520 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Energies |
| primary_location.landing_page_url | https://doi.org/10.3390/en16041628 |
| publication_date | 2023-02-06 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2912511381, https://openalex.org/W2167503549, https://openalex.org/W2083506732, https://openalex.org/W2331165628, https://openalex.org/W2083428643, https://openalex.org/W2170977282, https://openalex.org/W2006283899, https://openalex.org/W2945078999, https://openalex.org/W2776458183, https://openalex.org/W3028003652, https://openalex.org/W3012958665, https://openalex.org/W2940722387, https://openalex.org/W3105989526, https://openalex.org/W3033088344, https://openalex.org/W2082753519, https://openalex.org/W2006829425, https://openalex.org/W2069143585, https://openalex.org/W2964199361, https://openalex.org/W2941475335, https://openalex.org/W2064675550, https://openalex.org/W2470673105, https://openalex.org/W2800146313, https://openalex.org/W6757107679, https://openalex.org/W1996801827 |
| referenced_works_count | 24 |
| abstract_inverted_index.a | 3, 106, 182, 197, 212, 219 |
| abstract_inverted_index.an | 93, 152 |
| abstract_inverted_index.as | 77, 105, 136, 142 |
| abstract_inverted_index.by | 100 |
| abstract_inverted_index.in | 218 |
| abstract_inverted_index.of | 7, 26, 45, 47, 156, 166, 190, 194, 208 |
| abstract_inverted_index.on | 67, 144 |
| abstract_inverted_index.or | 72, 92, 110 |
| abstract_inverted_index.to | 12, 41, 113, 138, 200 |
| abstract_inverted_index.up | 115 |
| abstract_inverted_index.we | 50, 171 |
| abstract_inverted_index.All | 122 |
| abstract_inverted_index.GRU | 71 |
| abstract_inverted_index.SoC | 161 |
| abstract_inverted_index.The | 149, 206 |
| abstract_inverted_index.and | 34, 54, 74, 84, 131, 163, 170 |
| abstract_inverted_index.for | 19, 158, 175 |
| abstract_inverted_index.had | 181 |
| abstract_inverted_index.out | 126 |
| abstract_inverted_index.the | 14, 43, 116, 119, 128, 132, 139, 159, 167, 187, 191, 201, 204, 224 |
| abstract_inverted_index.via | 89 |
| abstract_inverted_index.was | 98, 124, 134, 186 |
| abstract_inverted_index.(EV) | 23 |
| abstract_inverted_index.Each | 96 |
| abstract_inverted_index.Five | 56 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.area | 193 |
| abstract_inverted_index.cell | 68 |
| abstract_inverted_index.from | 30, 60 |
| abstract_inverted_index.into | 211 |
| abstract_inverted_index.poor | 188 |
| abstract_inverted_index.rate | 108 |
| abstract_inverted_index.such | 76, 104 |
| abstract_inverted_index.than | 203 |
| abstract_inverted_index.this | 123 |
| abstract_inverted_index.were | 58 |
| abstract_inverted_index.with | 37, 70, 87 |
| abstract_inverted_index.Adam, | 81, 86 |
| abstract_inverted_index.Every | 178 |
| abstract_inverted_index.LSTM, | 73 |
| abstract_inverted_index.Using | 28 |
| abstract_inverted_index.based | 66 |
| abstract_inverted_index.could | 216 |
| abstract_inverted_index.data. | 148 |
| abstract_inverted_index.model | 180, 215 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.speed | 114 |
| abstract_inverted_index.state | 25, 44 |
| abstract_inverted_index.their | 52 |
| abstract_inverted_index.these | 209 |
| abstract_inverted_index.types | 69 |
| abstract_inverted_index.using | 127 |
| abstract_inverted_index.which | 185 |
| abstract_inverted_index.(RNNs) | 11 |
| abstract_inverted_index.96.56% | 157 |
| abstract_inverted_index.Nadam, | 82 |
| abstract_inverted_index.Robust | 85 |
| abstract_inverted_index.charge | 46 |
| abstract_inverted_index.custom | 214 |
| abstract_inverted_index.higher | 198 |
| abstract_inverted_index.layer. | 95 |
| abstract_inverted_index.method | 18, 97 |
| abstract_inverted_index.middle | 192 |
| abstract_inverted_index.model, | 221 |
| abstract_inverted_index.models | 29, 57 |
| abstract_inverted_index.neural | 9 |
| abstract_inverted_index.openly | 145 |
| abstract_inverted_index.result | 217 |
| abstract_inverted_index.single | 213 |
| abstract_inverted_index.weight | 199 |
| abstract_inverted_index.AdaMax, | 83 |
| abstract_inverted_index.average | 153 |
| abstract_inverted_index.battery | 147 |
| abstract_inverted_index.carried | 125 |
| abstract_inverted_index.charge, | 195 |
| abstract_inverted_index.charge. | 27 |
| abstract_inverted_index.closely | 137 |
| abstract_inverted_index.correct | 160 |
| abstract_inverted_index.driving | 39 |
| abstract_inverted_index.further | 176, 222 |
| abstract_inverted_index.machine | 16 |
| abstract_inverted_index.models, | 65 |
| abstract_inverted_index.overall | 168 |
| abstract_inverted_index.propose | 172 |
| abstract_inverted_index.results | 150 |
| abstract_inverted_index.several | 38, 164 |
| abstract_inverted_index.similar | 183 |
| abstract_inverted_index.sources | 33, 141 |
| abstract_inverted_index.testing | 36 |
| abstract_inverted_index.various | 61 |
| abstract_inverted_index.vehicle | 22 |
| abstract_inverted_index.voltage | 202 |
| abstract_inverted_index.accuracy | 53, 155 |
| abstract_inverted_index.applying | 101, 196 |
| abstract_inverted_index.assessed | 51 |
| abstract_inverted_index.calculus | 91 |
| abstract_inverted_index.current. | 205 |
| abstract_inverted_index.descent, | 80 |
| abstract_inverted_index.electric | 21 |
| abstract_inverted_index.examined | 99 |
| abstract_inverted_index.fitting, | 117 |
| abstract_inverted_index.gradient | 79 |
| abstract_inverted_index.learning | 17, 107 |
| abstract_inverted_index.momentum | 90 |
| abstract_inverted_index.multiple | 31 |
| abstract_inverted_index.networks | 10 |
| abstract_inverted_index.possible | 143 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.recovery | 112 |
| abstract_inverted_index.rollback | 111 |
| abstract_inverted_index.selected | 59 |
| abstract_inverted_index.training | 102 |
| abstract_inverted_index.accuracy. | 225 |
| abstract_inverted_index.attention | 94 |
| abstract_inverted_index.available | 146 |
| abstract_inverted_index.capturing | 189 |
| abstract_inverted_index.determine | 13, 42 |
| abstract_inverted_index.drawback, | 184 |
| abstract_inverted_index.drawbacks | 165 |
| abstract_inverted_index.improving | 223 |
| abstract_inverted_index.performed | 135 |
| abstract_inverted_index.potential | 173 |
| abstract_inverted_index.practical | 4 |
| abstract_inverted_index.published | 32, 62, 140 |
| abstract_inverted_index.recurrent | 8 |
| abstract_inverted_index.scenarios | 40 |
| abstract_inverted_index.scheduler | 109 |
| abstract_inverted_index.solutions | 174 |
| abstract_inverted_index.usability | 5 |
| abstract_inverted_index.TensorFlow | 129 |
| abstract_inverted_index.batteries, | 49 |
| abstract_inverted_index.drawbacks. | 55 |
| abstract_inverted_index.estimating | 20 |
| abstract_inverted_index.estimation | 64, 162 |
| abstract_inverted_index.extensions | 88 |
| abstract_inverted_index.framework, | 130 |
| abstract_inverted_index.optimisers | 75 |
| abstract_inverted_index.percentage | 154 |
| abstract_inverted_index.specifics. | 121 |
| abstract_inverted_index.stochastic | 78 |
| abstract_inverted_index.techniques | 103, 210 |
| abstract_inverted_index.best-suited | 15 |
| abstract_inverted_index.combination | 207 |
| abstract_inverted_index.highlighted | 151 |
| abstract_inverted_index.implemented | 179 |
| abstract_inverted_index.lithium-ion | 48 |
| abstract_inverted_index.batteries’ | 24 |
| abstract_inverted_index.highlighting | 118 |
| abstract_inverted_index.improvement. | 177 |
| abstract_inverted_index.better-suited | 220 |
| abstract_inverted_index.investigation | 6 |
| abstract_inverted_index.implementation | 120, 133 |
| abstract_inverted_index.implementation, | 169 |
| abstract_inverted_index.state-of-charge | 63 |
| abstract_inverted_index.cross-validation | 35 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5045671998 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I160993911 |
| citation_normalized_percentile.value | 0.75340954 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |