RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model Article Swipe

View

Xifeng Guo , Kaize Wang , Yao Shu , Guojiang Fu , Yi Ning ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1016/j.egyr.2023.05.121

With the wide application of lithium ion batteries, the importance of life prediction is also highlighted. The prediction of the remaining life of lithium ion battery is an important part of its health management, and accurate prediction can improve the safety of equipment. In this paper, a method for predicting the residual life of lithium ion batteries based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), One-dimensional Convolutional Neural Network (1D CNN) and Bi-directional Long Short-Term Memory (BiLSTM) neural network is proposed. The capacity is selected as the health factor, and then CEEMDAN is used to decompose the complex and unstable data to obtain stable components. One-dimensional Convolutional Neural Network (1D CNN) is used to deeply mine the capacity data of lithium-ion batteries. Finally, BiLSTM neural network modeling is used to predict the remaining useful life (RUL) of lithium-ion batteries. The NASA data set is used for testing and prediction comparison with BiLSTM model and CNN-BiLSTM model. The prediction results show that CEEMDAN-CNN BiLSTM model has higher prediction accuracy.

Related Topics

Convolutional Neural Network

Electric Battery

Computer Science

Lithium (Medication)

Artificial Intelligence

Concepts

Convolutional neural network Battery (electricity) Artificial neural network Noise (video) Computer science Hilbert–Huang transform Lithium (medication) Artificial intelligence Lithium-ion battery Battery capacity Pattern recognition (psychology) White noise Telecommunications Physics Power (physics) Medicine Endocrinology Image (mathematics) Quantum mechanics

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.egyr.2023.05.121
OA Status: gold
Cited By: 69
References: 7
Related Works: 10
OpenAlex ID: https://openalex.org/W4381191528

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4381191528

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.1016/j.egyr.2023.05.121

Digital Object Identifier
Title: RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model

Work title
Type: article

OpenAlex work type
Language: en

Primary language
Publication year: 2023

Year of publication
Publication date: 2023-06-19

Full publication date if available
Authors: Xifeng Guo, Kaize Wang, Yao Shu, Guojiang Fu, Yi Ning

List of authors in order
Landing page: https://doi.org/10.1016/j.egyr.2023.05.121

Publisher landing page
Open access: Yes

Whether a free full text is available
OA status: gold

Open access status per OpenAlex
OA URL: https://doi.org/10.1016/j.egyr.2023.05.121

Direct OA link when available
Concepts: Convolutional neural network, Battery (electricity), Artificial neural network, Noise (video), Computer science, Hilbert–Huang transform, Lithium (medication), Artificial intelligence, Lithium-ion battery, Battery capacity, Pattern recognition (psychology), White noise, Telecommunications, Physics, Power (physics), Medicine, Endocrinology, Image (mathematics), Quantum mechanics

Top concepts (fields/topics) attached by OpenAlex
Cited by: 69

Total citation count in OpenAlex
Citations by year (recent): 2025: 36, 2024: 27, 2023: 6

Per-year citation counts (last 5 years)
References (count): 7

Number of works referenced by this work
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4381191528
doi	https://doi.org/10.1016/j.egyr.2023.05.121
ids.doi	https://doi.org/10.1016/j.egyr.2023.05.121
ids.openalex	https://openalex.org/W4381191528
fwci	11.2857227
type	article
title	RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model
awards[0].id	https://openalex.org/G2110927219
awards[0].funder_id	https://openalex.org/F4320324679
awards[0].display_name
awards[0].funder_award_id	21-108-9-02
awards[0].funder_display_name	Shenyang Science and Technology Bureau
biblio.issue
biblio.volume	9
biblio.last_page	1306
biblio.first_page	1299
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/T10220
topics[1].field.id	https://openalex.org/fields/22
topics[1].field.display_name	Engineering
topics[1].score	0.9919999837875366
topics[1].domain.id	https://openalex.org/domains/3
topics[1].domain.display_name	Physical Sciences
topics[1].subfield.id	https://openalex.org/subfields/2207
topics[1].subfield.display_name	Control and Systems Engineering
topics[1].display_name	Machine Fault Diagnosis Techniques
topics[2].id	https://openalex.org/T10876
topics[2].field.id	https://openalex.org/fields/22
topics[2].field.display_name	Engineering
topics[2].score	0.9904999732971191
topics[2].domain.id	https://openalex.org/domains/3
topics[2].domain.display_name	Physical Sciences
topics[2].subfield.id	https://openalex.org/subfields/2207
topics[2].subfield.display_name	Control and Systems Engineering
topics[2].display_name	Fault Detection and Control Systems
funders[0].id	https://openalex.org/F4320324679
funders[0].ror	https://ror.org/007medz62
funders[0].display_name	Shenyang Science and Technology Bureau
is_xpac	False
apc_list.value	2090
apc_list.currency	USD
apc_list.value_usd	2090
apc_paid.value	2090
apc_paid.currency	USD
apc_paid.value_usd	2090
concepts[0].id	https://openalex.org/C81363708
concepts[0].level	2
concepts[0].score	0.6534034013748169
concepts[0].wikidata	https://www.wikidata.org/wiki/Q17084460
concepts[0].display_name	Convolutional neural network
concepts[1].id	https://openalex.org/C555008776
concepts[1].level	3
concepts[1].score	0.5884236097335815
concepts[1].wikidata	https://www.wikidata.org/wiki/Q267298
concepts[1].display_name	Battery (electricity)
concepts[2].id	https://openalex.org/C50644808
concepts[2].level	2
concepts[2].score	0.5863288044929504
concepts[2].wikidata	https://www.wikidata.org/wiki/Q192776
concepts[2].display_name	Artificial neural network
concepts[3].id	https://openalex.org/C99498987
concepts[3].level	3
concepts[3].score	0.5560358762741089
concepts[3].wikidata	https://www.wikidata.org/wiki/Q2210247
concepts[3].display_name	Noise (video)
concepts[4].id	https://openalex.org/C41008148
concepts[4].level	0
concepts[4].score	0.5466327667236328
concepts[4].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[4].display_name	Computer science
concepts[5].id	https://openalex.org/C25570617
concepts[5].level	3
concepts[5].score	0.5270105600357056
concepts[5].wikidata	https://www.wikidata.org/wiki/Q1006462
concepts[5].display_name	Hilbert–Huang transform
concepts[6].id	https://openalex.org/C2778541603
concepts[6].level	2
concepts[6].score	0.5061790347099304
concepts[6].wikidata	https://www.wikidata.org/wiki/Q152763
concepts[6].display_name	Lithium (medication)
concepts[7].id	https://openalex.org/C154945302
concepts[7].level	1
concepts[7].score	0.4798426330089569
concepts[7].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[7].display_name	Artificial intelligence
concepts[8].id	https://openalex.org/C2779197387
concepts[8].level	4
concepts[8].score	0.42968854308128357
concepts[8].wikidata	https://www.wikidata.org/wiki/Q2822895
concepts[8].display_name	Lithium-ion battery
concepts[9].id	https://openalex.org/C2989104859
concepts[9].level	4
concepts[9].score	0.42814725637435913
concepts[9].wikidata	https://www.wikidata.org/wiki/Q267298
concepts[9].display_name	Battery capacity
concepts[10].id	https://openalex.org/C153180895
concepts[10].level	2
concepts[10].score	0.3345497250556946
concepts[10].wikidata	https://www.wikidata.org/wiki/Q7148389
concepts[10].display_name	Pattern recognition (psychology)
concepts[11].id	https://openalex.org/C112633086
concepts[11].level	2
concepts[11].score	0.1359141767024994
concepts[11].wikidata	https://www.wikidata.org/wiki/Q381287
concepts[11].display_name	White noise
concepts[12].id	https://openalex.org/C76155785
concepts[12].level	1
concepts[12].score	0.06344428658485413
concepts[12].wikidata	https://www.wikidata.org/wiki/Q418
concepts[12].display_name	Telecommunications
concepts[13].id	https://openalex.org/C121332964
concepts[13].level	0
concepts[13].score	0.0
concepts[13].wikidata	https://www.wikidata.org/wiki/Q413
concepts[13].display_name	Physics
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/C71924100
concepts[15].level	0
concepts[15].score	0.0
concepts[15].wikidata	https://www.wikidata.org/wiki/Q11190
concepts[15].display_name	Medicine
concepts[16].id	https://openalex.org/C134018914
concepts[16].level	1
concepts[16].score	0.0
concepts[16].wikidata	https://www.wikidata.org/wiki/Q162606
concepts[16].display_name	Endocrinology
concepts[17].id	https://openalex.org/C115961682
concepts[17].level	2
concepts[17].score	0.0
concepts[17].wikidata	https://www.wikidata.org/wiki/Q860623
concepts[17].display_name	Image (mathematics)
concepts[18].id	https://openalex.org/C62520636
concepts[18].level	1
concepts[18].score	0.0
concepts[18].wikidata	https://www.wikidata.org/wiki/Q944
concepts[18].display_name	Quantum mechanics
keywords[0].id	https://openalex.org/keywords/convolutional-neural-network
keywords[0].score	0.6534034013748169
keywords[0].display_name	Convolutional neural network
keywords[1].id	https://openalex.org/keywords/battery
keywords[1].score	0.5884236097335815
keywords[1].display_name	Battery (electricity)
keywords[2].id	https://openalex.org/keywords/artificial-neural-network
keywords[2].score	0.5863288044929504
keywords[2].display_name	Artificial neural network
keywords[3].id	https://openalex.org/keywords/noise
keywords[3].score	0.5560358762741089
keywords[3].display_name	Noise (video)
keywords[4].id	https://openalex.org/keywords/computer-science
keywords[4].score	0.5466327667236328
keywords[4].display_name	Computer science
keywords[5].id	https://openalex.org/keywords/hilbert–huang-transform
keywords[5].score	0.5270105600357056
keywords[5].display_name	Hilbert–Huang transform
keywords[6].id	https://openalex.org/keywords/lithium
keywords[6].score	0.5061790347099304
keywords[6].display_name	Lithium (medication)
keywords[7].id	https://openalex.org/keywords/artificial-intelligence
keywords[7].score	0.4798426330089569
keywords[7].display_name	Artificial intelligence
keywords[8].id	https://openalex.org/keywords/lithium-ion-battery
keywords[8].score	0.42968854308128357
keywords[8].display_name	Lithium-ion battery
keywords[9].id	https://openalex.org/keywords/battery-capacity
keywords[9].score	0.42814725637435913
keywords[9].display_name	Battery capacity
keywords[10].id	https://openalex.org/keywords/pattern-recognition
keywords[10].score	0.3345497250556946
keywords[10].display_name	Pattern recognition (psychology)
keywords[11].id	https://openalex.org/keywords/white-noise
keywords[11].score	0.1359141767024994
keywords[11].display_name	White noise
keywords[12].id	https://openalex.org/keywords/telecommunications
keywords[12].score	0.06344428658485413
keywords[12].display_name	Telecommunications
language	en
locations[0].id	doi:10.1016/j.egyr.2023.05.121
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S2898434319
locations[0].source.issn	2352-4847
locations[0].source.type	journal
locations[0].source.is_oa	True
locations[0].source.issn_l	2352-4847
locations[0].source.is_core	True
locations[0].source.is_in_doaj	True
locations[0].source.display_name	Energy Reports
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	cc-by-nc-nd
locations[0].pdf_url
locations[0].version	publishedVersion
locations[0].raw_type	journal-article
locations[0].license_id	https://openalex.org/licenses/cc-by-nc-nd
locations[0].is_accepted	True
locations[0].is_published	True
locations[0].raw_source_name	Energy Reports
locations[0].landing_page_url	https://doi.org/10.1016/j.egyr.2023.05.121
locations[1].id	pmh:oai:doaj.org/article:057bdac627b043608d7722f57cf48c82
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	Energy Reports, Vol 9, Iss , Pp 1299-1306 (2023)
locations[1].landing_page_url	https://doaj.org/article/057bdac627b043608d7722f57cf48c82
indexed_in	crossref, doaj
authorships[0].author.id	https://openalex.org/A5101816847
authorships[0].author.orcid	https://orcid.org/0000-0003-3432-126X
authorships[0].author.display_name	Xifeng Guo
authorships[0].countries	CN
authorships[0].affiliations[0].institution_ids	https://openalex.org/I83714178
authorships[0].affiliations[0].raw_affiliation_string	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[0].institutions[0].id	https://openalex.org/I83714178
authorships[0].institutions[0].ror	https://ror.org/01zr73v18
authorships[0].institutions[0].type	education
authorships[0].institutions[0].lineage	https://openalex.org/I83714178
authorships[0].institutions[0].country_code	CN
authorships[0].institutions[0].display_name	Shenyang Jianzhu University
authorships[0].author_position	first
authorships[0].raw_author_name	Xifeng Guo
authorships[0].is_corresponding	False
authorships[0].raw_affiliation_strings	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[1].author.id	https://openalex.org/A5051104664
authorships[1].author.orcid
authorships[1].author.display_name	Kaize Wang
authorships[1].countries	CN
authorships[1].affiliations[0].institution_ids	https://openalex.org/I83714178
authorships[1].affiliations[0].raw_affiliation_string	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[1].institutions[0].id	https://openalex.org/I83714178
authorships[1].institutions[0].ror	https://ror.org/01zr73v18
authorships[1].institutions[0].type	education
authorships[1].institutions[0].lineage	https://openalex.org/I83714178
authorships[1].institutions[0].country_code	CN
authorships[1].institutions[0].display_name	Shenyang Jianzhu University
authorships[1].author_position	middle
authorships[1].raw_author_name	Kaize Wang
authorships[1].is_corresponding	True
authorships[1].raw_affiliation_strings	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[2].author.id	https://openalex.org/A5000956644
authorships[2].author.orcid	https://orcid.org/0000-0001-8847-0549
authorships[2].author.display_name	Yao Shu
authorships[2].countries	CN
authorships[2].affiliations[0].institution_ids	https://openalex.org/I83714178
authorships[2].affiliations[0].raw_affiliation_string	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[2].institutions[0].id	https://openalex.org/I83714178
authorships[2].institutions[0].ror	https://ror.org/01zr73v18
authorships[2].institutions[0].type	education
authorships[2].institutions[0].lineage	https://openalex.org/I83714178
authorships[2].institutions[0].country_code	CN
authorships[2].institutions[0].display_name	Shenyang Jianzhu University
authorships[2].author_position	middle
authorships[2].raw_author_name	Shu Yao
authorships[2].is_corresponding	False
authorships[2].raw_affiliation_strings	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[3].author.id	https://openalex.org/A5077204019
authorships[3].author.orcid
authorships[3].author.display_name	Guojiang Fu
authorships[3].countries	CN
authorships[3].affiliations[0].institution_ids	https://openalex.org/I83714178
authorships[3].affiliations[0].raw_affiliation_string	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[3].institutions[0].id	https://openalex.org/I83714178
authorships[3].institutions[0].ror	https://ror.org/01zr73v18
authorships[3].institutions[0].type	education
authorships[3].institutions[0].lineage	https://openalex.org/I83714178
authorships[3].institutions[0].country_code	CN
authorships[3].institutions[0].display_name	Shenyang Jianzhu University
authorships[3].author_position	middle
authorships[3].raw_author_name	Guojiang Fu
authorships[3].is_corresponding	False
authorships[3].raw_affiliation_strings	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[4].author.id	https://openalex.org/A5072489431
authorships[4].author.orcid	https://orcid.org/0000-0001-9851-0516
authorships[4].author.display_name	Yi Ning
authorships[4].countries	CN
authorships[4].affiliations[0].institution_ids	https://openalex.org/I83714178
authorships[4].affiliations[0].raw_affiliation_string	Shenyang Jianzhu University, Shenyang, 110168, China
authorships[4].institutions[0].id	https://openalex.org/I83714178
authorships[4].institutions[0].ror	https://ror.org/01zr73v18
authorships[4].institutions[0].type	education
authorships[4].institutions[0].lineage	https://openalex.org/I83714178
authorships[4].institutions[0].country_code	CN
authorships[4].institutions[0].display_name	Shenyang Jianzhu University
authorships[4].author_position	last
authorships[4].raw_author_name	Yi Ning
authorships[4].is_corresponding	False
authorships[4].raw_affiliation_strings	Shenyang Jianzhu University, Shenyang, 110168, China
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://doi.org/10.1016/j.egyr.2023.05.121
open_access.oa_status	gold
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model
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	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/W4308964873, https://openalex.org/W2768611900, https://openalex.org/W2995074223, https://openalex.org/W2357719166, https://openalex.org/W2729780499, https://openalex.org/W2384016584, https://openalex.org/W2543699259, https://openalex.org/W1983017669, https://openalex.org/W4224246090, https://openalex.org/W3085364154
cited_by_count	69
counts_by_year[0].year	2025
counts_by_year[0].cited_by_count	36
counts_by_year[1].year	2024
counts_by_year[1].cited_by_count	27
counts_by_year[2].year	2023
counts_by_year[2].cited_by_count	6
locations_count	2
best_oa_location.id	doi:10.1016/j.egyr.2023.05.121
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S2898434319
best_oa_location.source.issn	2352-4847
best_oa_location.source.type	journal
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l	2352-4847
best_oa_location.source.is_core	True
best_oa_location.source.is_in_doaj	True
best_oa_location.source.display_name	Energy Reports
best_oa_location.source.host_organization	https://openalex.org/P4310320990
best_oa_location.source.host_organization_name	Elsevier BV
best_oa_location.source.host_organization_lineage	https://openalex.org/P4310320990
best_oa_location.source.host_organization_lineage_names	Elsevier BV
best_oa_location.license	cc-by-nc-nd
best_oa_location.pdf_url
best_oa_location.version	publishedVersion
best_oa_location.raw_type	journal-article
best_oa_location.license_id	https://openalex.org/licenses/cc-by-nc-nd
best_oa_location.is_accepted	True
best_oa_location.is_published	True
best_oa_location.raw_source_name	Energy Reports
best_oa_location.landing_page_url	https://doi.org/10.1016/j.egyr.2023.05.121
primary_location.id	doi:10.1016/j.egyr.2023.05.121
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S2898434319
primary_location.source.issn	2352-4847
primary_location.source.type	journal
primary_location.source.is_oa	True
primary_location.source.issn_l	2352-4847
primary_location.source.is_core	True
primary_location.source.is_in_doaj	True
primary_location.source.display_name	Energy Reports
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	cc-by-nc-nd
primary_location.pdf_url
primary_location.version	publishedVersion
primary_location.raw_type	journal-article
primary_location.license_id	https://openalex.org/licenses/cc-by-nc-nd
primary_location.is_accepted	True
primary_location.is_published	True
primary_location.raw_source_name	Energy Reports
primary_location.landing_page_url	https://doi.org/10.1016/j.egyr.2023.05.121
publication_date	2023-06-19
publication_year	2023
referenced_works	https://openalex.org/W3086631322, https://openalex.org/W2064675550, https://openalex.org/W6799315883, https://openalex.org/W1921373351, https://openalex.org/W2896616144, https://openalex.org/W3045614568, https://openalex.org/W3184345533
referenced_works_count	7
abstract_inverted_index.a	46
abstract_inverted_index.In	43
abstract_inverted_index.an	27
abstract_inverted_index.as	88
abstract_inverted_index.is	13, 26, 82, 86, 95, 114, 130, 146
abstract_inverted_index.of	4, 10, 18, 22, 30, 41, 53, 122, 139
abstract_inverted_index.on	58
abstract_inverted_index.to	97, 104, 116, 132
abstract_inverted_index.(1D	72, 112
abstract_inverted_index.The	16, 84, 142, 159
abstract_inverted_index.and	34, 74, 92, 101, 150, 156
abstract_inverted_index.can	37
abstract_inverted_index.for	48, 148
abstract_inverted_index.has	167
abstract_inverted_index.ion	6, 24, 55
abstract_inverted_index.its	31
abstract_inverted_index.set	145
abstract_inverted_index.the	1, 8, 19, 39, 50, 89, 99, 119, 134
abstract_inverted_index.CNN)	73, 113
abstract_inverted_index.Long	76
abstract_inverted_index.Mode	62
abstract_inverted_index.NASA	143
abstract_inverted_index.With	0
abstract_inverted_index.also	14
abstract_inverted_index.data	103, 121, 144
abstract_inverted_index.life	11, 21, 52, 137
abstract_inverted_index.mine	118
abstract_inverted_index.part	29
abstract_inverted_index.show	162
abstract_inverted_index.that	163
abstract_inverted_index.then	93
abstract_inverted_index.this	44
abstract_inverted_index.used	96, 115, 131, 147
abstract_inverted_index.wide	2
abstract_inverted_index.with	64, 153
abstract_inverted_index.(RUL)	138
abstract_inverted_index.Noise	66
abstract_inverted_index.based	57
abstract_inverted_index.model	155, 166
abstract_inverted_index.BiLSTM	126, 154, 165
abstract_inverted_index.Memory	78
abstract_inverted_index.Neural	70, 110
abstract_inverted_index.deeply	117
abstract_inverted_index.health	32, 90
abstract_inverted_index.higher	168
abstract_inverted_index.method	47
abstract_inverted_index.model.	158
abstract_inverted_index.neural	80, 127
abstract_inverted_index.obtain	105
abstract_inverted_index.paper,	45
abstract_inverted_index.safety	40
abstract_inverted_index.stable	106
abstract_inverted_index.useful	136
abstract_inverted_index.CEEMDAN	94
abstract_inverted_index.Network	71, 111
abstract_inverted_index.battery	25
abstract_inverted_index.complex	100
abstract_inverted_index.factor,	91
abstract_inverted_index.improve	38
abstract_inverted_index.lithium	5, 23, 54
abstract_inverted_index.network	81, 128
abstract_inverted_index.predict	133
abstract_inverted_index.results	161
abstract_inverted_index.testing	149
abstract_inverted_index.(BiLSTM)	79
abstract_inverted_index.Adaptive	65
abstract_inverted_index.Complete	59
abstract_inverted_index.Ensemble	60
abstract_inverted_index.Finally,	125
abstract_inverted_index.accurate	35
abstract_inverted_index.capacity	85, 120
abstract_inverted_index.modeling	129
abstract_inverted_index.residual	51
abstract_inverted_index.selected	87
abstract_inverted_index.unstable	102
abstract_inverted_index.Empirical	61
abstract_inverted_index.accuracy.	170
abstract_inverted_index.batteries	56
abstract_inverted_index.decompose	98
abstract_inverted_index.important	28
abstract_inverted_index.proposed.	83
abstract_inverted_index.remaining	20, 135
abstract_inverted_index.(CEEMDAN),	67
abstract_inverted_index.CNN-BiLSTM	157
abstract_inverted_index.Short-Term	77
abstract_inverted_index.batteries,	7
abstract_inverted_index.batteries.	124, 141
abstract_inverted_index.comparison	152
abstract_inverted_index.equipment.	42
abstract_inverted_index.importance	9
abstract_inverted_index.predicting	49
abstract_inverted_index.prediction	12, 17, 36, 151, 160, 169
abstract_inverted_index.CEEMDAN-CNN	164
abstract_inverted_index.application	3
abstract_inverted_index.components.	107
abstract_inverted_index.lithium-ion	123, 140
abstract_inverted_index.management,	33
abstract_inverted_index.highlighted.	15
abstract_inverted_index.Convolutional	69, 109
abstract_inverted_index.Decomposition	63
abstract_inverted_index.Bi-directional	75
abstract_inverted_index.One-dimensional	68, 108
cited_by_percentile_year.max	100
cited_by_percentile_year.min	98
corresponding_author_ids	https://openalex.org/A5051104664
countries_distinct_count	1
institutions_distinct_count	5
corresponding_institution_ids	https://openalex.org/I83714178
sustainable_development_goals[0].id	https://metadata.un.org/sdg/12
sustainable_development_goals[0].score	0.4699999988079071
sustainable_development_goals[0].display_name	Responsible consumption and production
citation_normalized_percentile.value	0.98651382
citation_normalized_percentile.is_in_top_1_percent	True
citation_normalized_percentile.is_in_top_10_percent	True