State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network

To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conventional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neura...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Energy (Oxford) Ročník 227; s. 120451
Hlavní autori:	Chen, Junxiong, Feng, Xiong, Jiang, Lin, Zhu, Qiao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 15.07.2021 Elsevier BV
Predmet:	Data processing Denoising autoencoder Electric vehicle energy extracts Feature extraction Gated recurrent unit gates information processing Lithium lithium batteries Lithium-ion batteries Lithium-ion battery Neural networks Noise measurement Noise reduction processing technology Rechargeable batteries Recurrent neural network Recurrent neural networks State of charge State of charge estimation Lithium-ion battery Gated recurrent unit Denoising autoencoder State of charge estimation Electric vehicle Recurrent neural network
ISSN:	0360-5442, 1873-6785
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conventional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neural network (DAE-NN) is introduced to extract the useful data features by reducing the noise and increasing the dimensions of the battery measurement data. Then, the feature-extracted data is utilized to train the GRU-RNN, which is widely used in SOC estimation. Notice that a good input data processing method plays a key role in the SOC estimation performance and the generalization ability. Therefore, it is not trivial to combine the data processing method (DAE-NN), and the SOC estimation method (GRU-RNN), which is named DAE-GRU. Compared with the traditional GRU-RNN, the new DAE-GRU method shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the DAE-NN. Finally, three different driving cycles are given in the experiment to cross-train and verify the proposed DAE-GRU, GRU-RNN and RNN. Compared with the GRU-RNN and the RNN, it is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation. •A combined neural network method is proposed for the SOC estimation.•A DAE-NN is introduced to extract the useful battery data features.•A GRU-RNN is used to achieve the SOC estimation using the feature-extracted data.•The method exhibits accurate SOC estimation and excellent generalization ability.
AbstractList	To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conventional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neural network (DAE-NN) is introduced to extract the useful data features by reducing the noise and increasing the dimensions of the battery measurement data. Then, the feature-extracted data is utilized to train the GRU-RNN, which is widely used in SOC estimation. Notice that a good input data processing method plays a key role in the SOC estimation performance and the generalization ability. Therefore, it is not trivial to combine the data processing method (DAE-NN), and the SOC estimation method (GRU-RNN), which is named DAE-GRU. Compared with the traditional GRU-RNN, the new DAE-GRU method shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the DAE-NN. Finally, three different driving cycles are given in the experiment to cross-train and verify the proposed DAE-GRU, GRU-RNN and RNN. Compared with the GRU-RNN and the RNN, it is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation. To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conventional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neural network (DAE-NN) is introduced to extract the useful data features by reducing the noise and increasing the dimensions of the battery measurement data. Then, the feature-extracted data is utilized to train the GRU-RNN, which is widely used in SOC estimation. Notice that a good input data processing method plays a key role in the SOC estimation performance and the generalization ability. Therefore, it is not trivial to combine the data processing method (DAE-NN), and the SOC estimation method (GRU-RNN), which is named DAE-GRU. Compared with the traditional GRU-RNN, the new DAE-GRU method shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the DAE-NN. Finally, three different driving cycles are given in the experiment to cross-train and verify the proposed DAE-GRU, GRU-RNN and RNN. Compared with the GRU-RNN and the RNN, it is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation. •A combined neural network method is proposed for the SOC estimation.•A DAE-NN is introduced to extract the useful battery data features.•A GRU-RNN is used to achieve the SOC estimation using the feature-extracted data.•The method exhibits accurate SOC estimation and excellent generalization ability.
ArticleNumber	120451
Author	Jiang, Lin Feng, Xiong Zhu, Qiao Chen, Junxiong
Author_xml	– sequence: 1 givenname: Junxiong surname: Chen fullname: Chen, Junxiong email: cjxzlm@my.swjtu.edu.cn organization: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China – sequence: 2 givenname: Xiong surname: Feng fullname: Feng, Xiong email: tiga@my.swjtu.edu.cn organization: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China – sequence: 3 givenname: Lin surname: Jiang fullname: Jiang, Lin email: trover@163.com organization: Sichuan Aerospace System Engineering Research Institute, Chengdu, 610100, China – sequence: 4 givenname: Qiao surname: Zhu fullname: Zhu, Qiao email: zhuqiao@swjtu.edu.cn organization: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
BookMark	eNqFkTtvFDEUhS0UJDaBf0BhiYZmFr9mxqZAQhEkSJEoktSW176z8TJrBz-CtuKvx5uhQCmgur7WOUf2d07RSYgBEHpLyZoSOnzYrSFA2h7WjDC6poyInr5AKypH3g2j7E_QivCBdL0Q7BU6zXlHCOmlUiv0-7qYAjhO2N6ZtAUMufi9KT6G4-Xsy52v--64bkwpkA64Zh-22EGI_ulkaokQbHSQsAkOb1ugwwlsTQlCwTX48tcaoCYzt1F-xfTjNXo5mTnDmz_zDN1-_XJzftldfb_4dv75qrN8GEu34VJZNzopjLKWKaYko3bkozRiGuwgTe-sooNgynA3qZEzwh2IDQzWcdnzM_R-yb1P8Wdtn9R7ny3MswkQa9asHwVv5AbVpO-eSXexptBe11SC9D0hgjSVWFQ2xZwTTPo-NXDpoCnRx1b0Ti-t6GMremml2T4-s1lfnnCXZPz8P_OnxQyN1IOHpLP1DT043_gW7aL_d8Aj4oyvEg
CitedBy_id	crossref_primary_10_3390_pr11030887 crossref_primary_10_3390_en16010023 crossref_primary_10_3390_condmat6030028 crossref_primary_10_3390_en15134670 crossref_primary_10_1016_j_engappai_2023_107698 crossref_primary_10_1016_j_epsr_2024_110284 crossref_primary_10_1016_j_est_2024_111626 crossref_primary_10_1016_j_est_2025_115833 crossref_primary_10_1016_j_egyai_2025_100585 crossref_primary_10_1039_D2SE01209J crossref_primary_10_1016_j_energy_2022_125375 crossref_primary_10_1016_j_apenergy_2024_122666 crossref_primary_10_1016_j_energy_2022_123233 crossref_primary_10_1016_j_ress_2023_109123 crossref_primary_10_1007_s00202_025_03235_z crossref_primary_10_1016_j_est_2023_108974 crossref_primary_10_1016_j_est_2022_104664 crossref_primary_10_1016_j_energy_2025_137539 crossref_primary_10_3390_ma17174193 crossref_primary_10_1016_j_energy_2024_132583 crossref_primary_10_1007_s43236_021_00294_w crossref_primary_10_1016_j_est_2023_110004 crossref_primary_10_1109_ACCESS_2024_3408220 crossref_primary_10_1016_j_est_2025_116899 crossref_primary_10_1016_j_renene_2022_08_123 crossref_primary_10_3390_s23062924 crossref_primary_10_1109_ACCESS_2023_3326725 crossref_primary_10_3390_en16042008 crossref_primary_10_1016_j_jobe_2023_105961 crossref_primary_10_1016_j_engappai_2025_110913 crossref_primary_10_1016_j_est_2021_103319 crossref_primary_10_1016_j_apenergy_2025_126040 crossref_primary_10_1016_j_procs_2025_04_108 crossref_primary_10_1016_j_rser_2023_114262 crossref_primary_10_1016_j_est_2025_115575 crossref_primary_10_1016_j_est_2024_111527 crossref_primary_10_1007_s11431_021_2068_8 crossref_primary_10_1016_j_apenergy_2024_123056 crossref_primary_10_1007_s11581_024_05811_y crossref_primary_10_1016_j_jpowsour_2022_231441 crossref_primary_10_1016_j_etran_2024_100322 crossref_primary_10_1016_j_est_2024_112571 crossref_primary_10_1016_j_energy_2021_121236 crossref_primary_10_1016_j_energy_2025_136264 crossref_primary_10_1002_we_2761 crossref_primary_10_1007_s00202_024_02371_2 crossref_primary_10_3390_en15062064 crossref_primary_10_1109_TTE_2023_3334826 crossref_primary_10_1007_s11581_025_06560_2 crossref_primary_10_1016_j_energy_2024_133417 crossref_primary_10_1016_j_est_2024_112107 crossref_primary_10_1016_j_est_2023_107495 crossref_primary_10_1016_j_jpowsour_2024_235312 crossref_primary_10_1016_j_est_2023_107650 crossref_primary_10_1016_j_energy_2022_124915 crossref_primary_10_1109_ACCESS_2024_3509472 crossref_primary_10_1016_j_energy_2023_127409 crossref_primary_10_1016_j_energy_2024_130492 crossref_primary_10_1016_j_est_2023_107728 crossref_primary_10_1016_j_est_2022_105384 crossref_primary_10_1016_j_apenergy_2022_120037 crossref_primary_10_1016_j_est_2025_116202 crossref_primary_10_1016_j_est_2024_110575 crossref_primary_10_1016_j_jpowsour_2024_235272 crossref_primary_10_1016_j_est_2023_108774 crossref_primary_10_3390_jmse11010200 crossref_primary_10_1016_j_jpowsour_2024_235827 crossref_primary_10_1149_1945_7111_adee4d crossref_primary_10_1016_j_est_2023_106904 crossref_primary_10_1016_j_energy_2023_130025 crossref_primary_10_1016_j_energy_2025_137974 crossref_primary_10_1016_j_est_2022_105396 crossref_primary_10_1016_j_fuel_2023_129797 crossref_primary_10_1016_j_apenergy_2022_120043 crossref_primary_10_1016_j_est_2024_112887 crossref_primary_10_3233_JIFS_232465 crossref_primary_10_3390_wevj16060291 crossref_primary_10_1002_er_8596 crossref_primary_10_1016_j_est_2023_109056 crossref_primary_10_1016_j_est_2023_109696 crossref_primary_10_1016_j_etran_2024_100378 crossref_primary_10_1016_j_egyr_2023_01_109 crossref_primary_10_1051_e3sconf_202340904008 crossref_primary_10_1002_adma_202101474 crossref_primary_10_1016_j_energy_2023_129801 crossref_primary_10_1016_j_est_2023_107677 crossref_primary_10_1016_j_energy_2022_124933 crossref_primary_10_1016_j_energy_2022_124538 crossref_primary_10_3390_wevj13090159 crossref_primary_10_1103_PhysRevD_111_024067 crossref_primary_10_1109_TTE_2023_3291501 crossref_primary_10_3390_fractalfract7050391 crossref_primary_10_1016_j_jpowsour_2024_235374 crossref_primary_10_1109_TVT_2023_3267500 crossref_primary_10_1016_j_egyr_2022_07_017 crossref_primary_10_1016_j_energy_2024_130913 crossref_primary_10_1016_j_est_2024_113986 crossref_primary_10_1016_j_eswa_2023_121609 crossref_primary_10_1016_j_est_2025_115934 crossref_primary_10_1109_TII_2023_3316289 crossref_primary_10_1088_1361_6501_ac95b2 crossref_primary_10_3390_app14156648 crossref_primary_10_1016_j_etran_2023_100286 crossref_primary_10_1016_j_energy_2021_122406 crossref_primary_10_1016_j_rser_2025_115803 crossref_primary_10_1109_TEC_2024_3446168 crossref_primary_10_1016_j_engappai_2024_107946 crossref_primary_10_1016_j_energy_2023_127846 crossref_primary_10_1016_j_est_2023_109196 crossref_primary_10_3390_batteries10030089 crossref_primary_10_1016_j_est_2023_107296 crossref_primary_10_3390_app12052305 crossref_primary_10_1016_j_est_2023_108421 crossref_primary_10_1002_er_7643 crossref_primary_10_1002_ente_202200921
Cites_doi	10.1016/j.rser.2019.06.040 10.1016/j.rser.2019.109334 10.1109/ACCESS.2018.2797976 10.3390/en12091592 10.1016/j.neucom.2015.02.096 10.1016/j.jclepro.2020.124110 10.1016/j.energy.2020.119025 10.1016/j.apenergy.2020.115494 10.1016/j.jpowsour.2015.01.145 10.1016/j.jpowsour.2018.06.104 10.1016/j.energy.2019.03.059 10.1162/neco.1997.9.8.1735 10.1016/j.jpowsour.2019.227558 10.1007/s00521-016-2790-x 10.1016/j.est.2020.101980 10.1016/j.jpowsour.2020.228691 10.1016/j.jpowsour.2020.228375 10.1016/j.est.2020.101459 10.1016/j.energy.2019.116538 10.1016/j.est.2020.101978 10.1109/72.279181
ContentType	Journal Article
Copyright	2021 Elsevier Ltd Copyright Elsevier BV Jul 15, 2021
Copyright_xml	– notice: 2021 Elsevier Ltd – notice: Copyright Elsevier BV Jul 15, 2021
DBID	AAYXX CITATION 7SP 7ST 7TB 8FD C1K F28 FR3 KR7 L7M SOI 7S9 L.6
DOI	10.1016/j.energy.2021.120451
DatabaseName	CrossRef Electronics & Communications Abstracts Environment Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Environmental Sciences and Pollution Management ANTE: Abstracts in New Technology & Engineering Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace Environment Abstracts AGRICOLA AGRICOLA - Academic
DatabaseTitle	CrossRef Civil Engineering Abstracts Technology Research Database Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts Engineering Research Database Environment Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Environmental Sciences and Pollution Management AGRICOLA AGRICOLA - Academic
DatabaseTitleList	Civil Engineering Abstracts AGRICOLA
DeliveryMethod	fulltext_linktorsrc
Discipline	Economics Environmental Sciences
EISSN	1873-6785
ExternalDocumentID	10_1016_j_energy_2021_120451 S0360544221007003
GroupedDBID	--K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARJD AAXUO ABJNI ABMAC ABYKQ ACDAQ ACGFS ACIWK ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHIDL AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SDF SDG SES SPC SPCBC SSR SSZ T5K TN5 XPP ZMT ~02 ~G- 29G 6TJ 9DU AAHBH AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABFNM ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADXHL AEIPS AEUPX AFJKZ AFPUW AGQPQ AHHHB AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HVGLF HZ~ R2- SAC SEW WUQ ~HD 7SP 7ST 7TB 8FD AGCQF C1K F28 FR3 KR7 L7M SOI 7S9 L.6
ID	FETCH-LOGICAL-c367t-b389cd7d84a9cc2929821c7378a4f6c68a5dc916429a3df973203de4be6cd3853
ISICitedReferencesCount	148
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000652610900009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0360-5442
IngestDate	Wed Oct 01 13:28:09 EDT 2025 Wed Aug 13 09:31:31 EDT 2025 Sat Nov 29 07:21:56 EST 2025 Tue Nov 18 21:49:27 EST 2025 Fri Feb 23 02:46:38 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Lithium-ion battery Gated recurrent unit Denoising autoencoder State of charge estimation Electric vehicle Recurrent neural network
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c367t-b389cd7d84a9cc2929821c7378a4f6c68a5dc916429a3df973203de4be6cd3853
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PQID	2540550040
PQPubID	2045484
ParticipantIDs	proquest_miscellaneous_2574312069 proquest_journals_2540550040 crossref_primary_10_1016_j_energy_2021_120451 crossref_citationtrail_10_1016_j_energy_2021_120451 elsevier_sciencedirect_doi_10_1016_j_energy_2021_120451
PublicationCentury	2000
PublicationDate	2021-07-15
PublicationDateYYYYMMDD	2021-07-15
PublicationDate_xml	– month: 07 year: 2021 text: 2021-07-15 day: 15
PublicationDecade	2020
PublicationPlace	Oxford
PublicationPlace_xml	– name: Oxford
PublicationTitle	Energy (Oxford)
PublicationYear	2021
Publisher	Elsevier Ltd Elsevier BV
Publisher_xml	– name: Elsevier Ltd – name: Elsevier BV
References	Zhu, Duan, Zhang, Zhang, Zhang (bib5) 2020; 277 Hannan, Lipu, Hussain, Saad, Ayob (bib10) 2018; 6 Yang, Li, Li, Miao (bib16) 2019; 175 Li, Xiao, Fan (bib17) 2019; 12 Hu, Feng, Liu, Zhang, Xie, Liu (bib1) 2019; 114 Hossain Lipu, Hannan, Hussain, Ayob, Saad, Karim, How (bib2) 2020; 277 Ben Sassi, Errahimi, ES-Sbai (bib7) 2020; 32 Goodfellow, Bengio, Courville (bib21) 2016 Chemali, Kollmeyer, Preindl, Emadi (bib9) 2018; 400 Meng, Ding, Zhang, Zhang (bib24) 2018; 30 Gorgel, Simsek (bib22) 2019; 355 K. Cho, B. van Merriënboer, D. Bahdanau, Y. Bengio, On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, arXiv e-prints abs/1409.1259, URL Fasahat, Manthouri (bib15) 2020; 469 Bian, He, Yang, Huang (bib13) 2020; 449 Hochreiter, Schmidhuber (bib19) 1997; 9 Bian, He, Yang (bib12) 2020; 191 Bengio, Simard, Frasconi (bib18) 1994; 5 Sheng, Xiao (bib8) 2015; 281 Sun, Yu, Wang, Zhang, Huang, Zhou, Amietszajew, Bhagat (bib4) 2021; 214 Zhang, Guo, Zhang (bib6) 2020; 32 . le Cao, bing Huang, chun Sun (bib23) 2016; 174 Hong, Wang, Chen, Wang, Qu (bib11) 2020; 30 Shrivastava, Soon, Idris, Mekhilef (bib3) 2019; 113 Ma, Wang, Yang, Cheng, Lu, Tao, Zhou (bib14) 2020; 474 Hannan (10.1016/j.energy.2021.120451_bib10) 2018; 6 le Cao (10.1016/j.energy.2021.120451_bib23) 2016; 174 Sun (10.1016/j.energy.2021.120451_bib4) 2021; 214 Ma (10.1016/j.energy.2021.120451_bib14) 2020; 474 Yang (10.1016/j.energy.2021.120451_bib16) 2019; 175 Ben Sassi (10.1016/j.energy.2021.120451_bib7) 2020; 32 Goodfellow (10.1016/j.energy.2021.120451_bib21) 2016 Hochreiter (10.1016/j.energy.2021.120451_bib19) 1997; 9 Zhang (10.1016/j.energy.2021.120451_bib6) 2020; 32 Sheng (10.1016/j.energy.2021.120451_bib8) 2015; 281 Hossain Lipu (10.1016/j.energy.2021.120451_bib2) 2020; 277 Shrivastava (10.1016/j.energy.2021.120451_bib3) 2019; 113 Bengio (10.1016/j.energy.2021.120451_bib18) 1994; 5 Zhu (10.1016/j.energy.2021.120451_bib5) 2020; 277 Hong (10.1016/j.energy.2021.120451_bib11) 2020; 30 Li (10.1016/j.energy.2021.120451_bib17) 2019; 12 Gorgel (10.1016/j.energy.2021.120451_bib22) 2019; 355 10.1016/j.energy.2021.120451_bib20 Hu (10.1016/j.energy.2021.120451_bib1) 2019; 114 Chemali (10.1016/j.energy.2021.120451_bib9) 2018; 400 Bian (10.1016/j.energy.2021.120451_bib13) 2020; 449 Bian (10.1016/j.energy.2021.120451_bib12) 2020; 191 Fasahat (10.1016/j.energy.2021.120451_bib15) 2020; 469 Meng (10.1016/j.energy.2021.120451_bib24) 2018; 30
References_xml	– volume: 277 start-page: 115494 year: 2020 ident: bib5 article-title: Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter publication-title: Appl Energy – year: 2016 ident: bib21 article-title: Deep learning – volume: 355 start-page: 325 year: 2019 end-page: 342 ident: bib22 article-title: Face recognition via deep stacked denoising sparse autoencoders (DSDSA) publication-title: Appl Math Comput – volume: 174 start-page: 60 year: 2016 end-page: 71 ident: bib23 article-title: Building feature space of extreme learning machine with sparse denoising stacked-autoencoder publication-title: Neurocomputing – volume: 281 start-page: 131 year: 2015 end-page: 137 ident: bib8 article-title: Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine publication-title: J Power Sources – volume: 449 start-page: 227558 year: 2020 ident: bib13 article-title: State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture publication-title: J Power Sources – volume: 114 start-page: 109334 year: 2019 ident: bib1 article-title: State estimation for advanced battery management: key challenges and future trends publication-title: Renew Sustain Energy Rev – volume: 30 start-page: 2083 year: 2018 end-page: 2100 ident: bib24 article-title: Research of stacked denoising sparse autoencoder publication-title: Neural Comput Appl – volume: 6 start-page: 10069 year: 2018 end-page: 10079 ident: bib10 article-title: Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm publication-title: IEEE Access – volume: 12 start-page: 1592 year: 2019 ident: bib17 article-title: An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit publication-title: Energies – volume: 32 start-page: 101980 year: 2020 ident: bib6 article-title: An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery publication-title: J Energy Storage – reference: K. Cho, B. van Merriënboer, D. Bahdanau, Y. Bengio, On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, arXiv e-prints abs/1409.1259, URL – volume: 191 start-page: 116538 year: 2020 ident: bib12 article-title: Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries publication-title: Energy – volume: 175 start-page: 66 year: 2019 end-page: 75 ident: bib16 article-title: State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network publication-title: Energy – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib19 article-title: Long short-term memory publication-title: Neural Comput – volume: 113 start-page: 109233 year: 2019 ident: bib3 article-title: Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries publication-title: Renew Sustain Energy Rev – volume: 32 start-page: 101978 year: 2020 ident: bib7 article-title: State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications publication-title: J Energy Storage – volume: 400 start-page: 242 year: 2018 end-page: 255 ident: bib9 article-title: State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach publication-title: J Power Sources – volume: 214 start-page: 119025 year: 2021 ident: bib4 article-title: State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator publication-title: Energy – volume: 30 start-page: 101459 year: 2020 ident: bib11 article-title: Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles publication-title: J Energy Storage – reference: . – volume: 474 start-page: 228691 year: 2020 ident: bib14 article-title: Robust state of charge estimation based on a sequence-to-sequence mapping model with process information publication-title: J Power Sources – volume: 277 start-page: 124110 year: 2020 ident: bib2 article-title: Data-driven state of charge estimation of lithium-ion batteries: algorithms, implementation factors, limitations and future trends publication-title: J Clean Prod – volume: 5 start-page: 157 year: 1994 end-page: 166 ident: bib18 article-title: Learning long-term dependencies with gradient descent is difficult publication-title: IEEE Trans Neural Network – volume: 469 start-page: 228375 year: 2020 ident: bib15 article-title: State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks publication-title: J Power Sources – volume: 355 start-page: 325 year: 2019 ident: 10.1016/j.energy.2021.120451_bib22 article-title: Face recognition via deep stacked denoising sparse autoencoders (DSDSA) publication-title: Appl Math Comput – volume: 113 start-page: 109233 year: 2019 ident: 10.1016/j.energy.2021.120451_bib3 article-title: Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.06.040 – volume: 114 start-page: 109334 year: 2019 ident: 10.1016/j.energy.2021.120451_bib1 article-title: State estimation for advanced battery management: key challenges and future trends publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.109334 – volume: 6 start-page: 10069 year: 2018 ident: 10.1016/j.energy.2021.120451_bib10 article-title: Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2797976 – volume: 12 start-page: 1592 issue: 9 year: 2019 ident: 10.1016/j.energy.2021.120451_bib17 article-title: An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit publication-title: Energies doi: 10.3390/en12091592 – volume: 174 start-page: 60 year: 2016 ident: 10.1016/j.energy.2021.120451_bib23 article-title: Building feature space of extreme learning machine with sparse denoising stacked-autoencoder publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.02.096 – year: 2016 ident: 10.1016/j.energy.2021.120451_bib21 – volume: 277 start-page: 124110 year: 2020 ident: 10.1016/j.energy.2021.120451_bib2 article-title: Data-driven state of charge estimation of lithium-ion batteries: algorithms, implementation factors, limitations and future trends publication-title: J Clean Prod doi: 10.1016/j.jclepro.2020.124110 – volume: 214 start-page: 119025 year: 2021 ident: 10.1016/j.energy.2021.120451_bib4 article-title: State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator publication-title: Energy doi: 10.1016/j.energy.2020.119025 – volume: 277 start-page: 115494 year: 2020 ident: 10.1016/j.energy.2021.120451_bib5 article-title: Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115494 – volume: 281 start-page: 131 year: 2015 ident: 10.1016/j.energy.2021.120451_bib8 article-title: Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine publication-title: J Power Sources doi: 10.1016/j.jpowsour.2015.01.145 – volume: 400 start-page: 242 year: 2018 ident: 10.1016/j.energy.2021.120451_bib9 article-title: State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach publication-title: J Power Sources doi: 10.1016/j.jpowsour.2018.06.104 – volume: 175 start-page: 66 year: 2019 ident: 10.1016/j.energy.2021.120451_bib16 article-title: State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network publication-title: Energy doi: 10.1016/j.energy.2019.03.059 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.energy.2021.120451_bib19 article-title: Long short-term memory publication-title: Neural Comput doi: 10.1162/neco.1997.9.8.1735 – volume: 449 start-page: 227558 year: 2020 ident: 10.1016/j.energy.2021.120451_bib13 article-title: State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture publication-title: J Power Sources doi: 10.1016/j.jpowsour.2019.227558 – volume: 30 start-page: 2083 issue: 7 year: 2018 ident: 10.1016/j.energy.2021.120451_bib24 article-title: Research of stacked denoising sparse autoencoder publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2790-x – ident: 10.1016/j.energy.2021.120451_bib20 – volume: 32 start-page: 101980 year: 2020 ident: 10.1016/j.energy.2021.120451_bib6 article-title: An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery publication-title: J Energy Storage doi: 10.1016/j.est.2020.101980 – volume: 474 start-page: 228691 year: 2020 ident: 10.1016/j.energy.2021.120451_bib14 article-title: Robust state of charge estimation based on a sequence-to-sequence mapping model with process information publication-title: J Power Sources doi: 10.1016/j.jpowsour.2020.228691 – volume: 469 start-page: 228375 year: 2020 ident: 10.1016/j.energy.2021.120451_bib15 article-title: State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks publication-title: J Power Sources doi: 10.1016/j.jpowsour.2020.228375 – volume: 30 start-page: 101459 year: 2020 ident: 10.1016/j.energy.2021.120451_bib11 article-title: Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles publication-title: J Energy Storage doi: 10.1016/j.est.2020.101459 – volume: 191 start-page: 116538 year: 2020 ident: 10.1016/j.energy.2021.120451_bib12 article-title: Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries publication-title: Energy doi: 10.1016/j.energy.2019.116538 – volume: 32 start-page: 101978 year: 2020 ident: 10.1016/j.energy.2021.120451_bib7 article-title: State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications publication-title: J Energy Storage doi: 10.1016/j.est.2020.101978 – volume: 5 start-page: 157 issue: 2 year: 1994 ident: 10.1016/j.energy.2021.120451_bib18 article-title: Learning long-term dependencies with gradient descent is difficult publication-title: IEEE Trans Neural Network doi: 10.1109/72.279181
SSID	ssj0005899
Score	2.6494398
Snippet	To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	120451
SubjectTerms	Data processing Denoising autoencoder Electric vehicle energy extracts Feature extraction Gated recurrent unit gates information processing Lithium lithium batteries Lithium-ion batteries Lithium-ion battery Neural networks Noise measurement Noise reduction processing technology Rechargeable batteries Recurrent neural network Recurrent neural networks State of charge State of charge estimation
Title	State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network
URI	https://dx.doi.org/10.1016/j.energy.2021.120451 https://www.proquest.com/docview/2540550040 https://www.proquest.com/docview/2574312069
Volume	227
WOSCitedRecordID	wos000652610900009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6785 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005899 issn: 0360-5442 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLbKhgQXBIOJsoGMhLhUqVrnh5PjNHWCqSogdVLFxUoch2XakrI2VXfiX-c923E7fmjjwCVKXCdN874-f3557zMh77IwinmSxV4Wq8KD8Rolb4PCC-WQ8yT0VVEM9GITfDKJZ7Pkc6ezamthVpe8quL1Opn_V1NDGxgbS2f_wdzuotAA-2B02ILZYXsvw2v6qLPFUQRJ9VBG48oRQ2Dd52Vz5eFhprU1b3qNjheAA6pLvZc2yxr1LVFmAuPqGGnDIhdptZwacANbhyiJCYauTEL5rVC_KSxERdO1SaJ3YYdjVxVSreFevrXts9LmCMMzdY2npY1qj0uH5a_nDbZ8KdN6O3LBhhgSNbWbrmJr4IVBcMsbM8Z78_4QVfKH3h99vAk3XPSV_g19vLLtvxnT2vf4k0_i5Gw8FtPRbPp-_t3D1cbwrbxdeuUB2WU8TMAb7h59HM1ON8lBsV551N1hW3SpMwN__-K_kZpfhnfNWaZPyRM72aBHBiTPSEdVe-RRW4u-2CP7o02dI3S0jn7xnPzQKKJ1QQ2K6AZF2LiFImpRRDWKqEMR3UIRBRRRjSLqYEMRRVuHBkXUougFOTsZTY8_eHatDk_6EV96GRBfmfM8DtJESgakO2ZDyX0ep0ERyShOw1zCVAToT-rnBWpEDfxcBZmKZO4DZ9wnO1VdqZeEMharQDKZ5dIPMhgieKZSpng6yBJg10mX-O3DFtIK2eN6KpeizVi8EMZEAk0kjIm6xHNnzY2Qyx39eWtHYcmoIZkCcHjHmYet2YX1CwvBcGYU4pDZJW_dx-DK8f1cWqm6wT5I59kgSl7do88Bebz5Ux2SneV1o16Th3K1LBfXbyygfwIflsXG
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=State+of+charge+estimation+of+lithium-ion+battery+using+denoising+autoencoder+and+gated+recurrent+unit+recurrent+neural+network&rft.jtitle=Energy+%28Oxford%29&rft.au=Chen%2C+Junxiong&rft.au=Xiong%2C+Feng&rft.au=Jiang%2C+Lin&rft.au=Zhu%2C+Qiao&rft.date=2021-07-15&rft.issn=0360-5442&rft.volume=227+p.120451-&rft_id=info:doi/10.1016%2Fj.energy.2021.120451&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0360-5442&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0360-5442&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0360-5442&client=summon