Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction
Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) i...
Gespeichert in:
| Veröffentlicht in: | iScience Jg. 25; H. 4; S. 103988 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Elsevier Inc
15.04.2022
Elsevier |
| Schlagworte: | |
| ISSN: | 2589-0042, 2589-0042 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.
[Display omitted]
•An ISSA-DELM method is used to predict the battery state-of-health•Two indirect health indicators are extracted from the partial discharging data•EOBL and Cauchy-Gaussian mutation strategy are utilized to improve SSA•The proposed approach obtains better prediction accuracy in shorter computation time
Machine learning; Energy management |
|---|---|
| AbstractList | Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.
[Display omitted]
•An ISSA-DELM method is used to predict the battery state-of-health•Two indirect health indicators are extracted from the partial discharging data•EOBL and Cauchy-Gaussian mutation strategy are utilized to improve SSA•The proposed approach obtains better prediction accuracy in shorter computation time
Machine learning; Energy management Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods. • An ISSA-DELM method is used to predict the battery state-of-health • Two indirect health indicators are extracted from the partial discharging data • EOBL and Cauchy-Gaussian mutation strategy are utilized to improve SSA • The proposed approach obtains better prediction accuracy in shorter computation time Machine learning; Energy management Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods. Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods. |
| ArticleNumber | 103988 |
| Author | Shi, Yuanhao Pang, Xiaoqiong Jia, Jianfang Yuan, Shufang Wen, Jie Zeng, Jianchao |
| Author_xml | – sequence: 1 givenname: Jianfang orcidid: 0000-0001-6117-6649 surname: Jia fullname: Jia, Jianfang email: jiajianfang@nuc.edu.cn organization: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China – sequence: 2 givenname: Shufang surname: Yuan fullname: Yuan, Shufang organization: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China – sequence: 3 givenname: Yuanhao surname: Shi fullname: Shi, Yuanhao organization: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China – sequence: 4 givenname: Jie orcidid: 0000-0003-0302-4123 surname: Wen fullname: Wen, Jie organization: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China – sequence: 5 givenname: Xiaoqiong surname: Pang fullname: Pang, Xiaoqiong organization: School of Data Science and Technology, North University of China, Taiyuan 030051, China – sequence: 6 givenname: Jianchao surname: Zeng fullname: Zeng, Jianchao organization: School of Data Science and Technology, North University of China, Taiyuan 030051, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35310948$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktr3DAUhU1JadI0f6CLomU3nkryQzKUQgl9DAS6adfiWroaa7AtV9ZMmm761yvHSUm6CAgkXZ3zXdC5L7OT0Y-YZa8Z3TDK6nf7jZu123DKeSoUjZTPsjNeySantOQnD86n2cU87ymlPK2yqV9kp0VVMNqU8iz7sx2m4I9oyDxBCP6azAhBdwT6nQ8udgPxU3SD-w3R-ZEYxIngrxhwQNIn6ejGHRlAd25EYn0gfTK5w5Av6hZixHBD5ggRc2_zDqGPHZkCGqcX4KvsuYV-xou7_Tz78fnT98uv-dW3L9vLj1e5rjiLuRaWicbaggPTjJfAhJECCtSmBM0Yr6CkYOq6tibdmBTcLBahtWgYt8V5tl25xsNeTcENEG6UB6duCz7sFITodI8KpLam5G1leVsKYZpWU2mkxULUbYl1Yn1YWdOhHdBoHGOA_hH08cvoOrXzRyUbLhq-AN7eAYL_ecA5qiGFiX0PI_rDrHhdsoqJmjZJ-uZhr39N7hNMArkKdPDzHNAq7eJtVqm16xWjapkXtVfLvKhlXtQ6L8nK_7Pe0580vV9NmNI6OgwqKXDUKdCAOqbvdE_Z_wLGjN0k |
| CitedBy_id | crossref_primary_10_1016_j_physa_2023_129355 crossref_primary_10_1016_j_energy_2023_128094 crossref_primary_10_1016_j_eswa_2023_122042 crossref_primary_10_1016_j_jag_2023_103644 crossref_primary_10_1016_j_energy_2025_134810 crossref_primary_10_3390_su15129176 crossref_primary_10_3390_electronics13203991 crossref_primary_10_1051_ijmqe_2024012 crossref_primary_10_1155_2022_6486876 crossref_primary_10_1016_j_est_2023_107161 crossref_primary_10_3390_wevj16070356 crossref_primary_10_1016_j_est_2023_109069 crossref_primary_10_1016_j_est_2024_113086 crossref_primary_10_1109_ACCESS_2023_3320058 crossref_primary_10_1007_s11071_024_10815_4 crossref_primary_10_1007_s11831_023_09975_0 crossref_primary_10_1016_j_est_2023_108732 crossref_primary_10_1093_ce_zkad054 crossref_primary_10_1016_j_physa_2024_129870 crossref_primary_10_3390_biomimetics8020182 crossref_primary_10_1016_j_renene_2022_07_016 crossref_primary_10_1088_1361_6501_ad1ba0 crossref_primary_10_1016_j_microrel_2023_114975 crossref_primary_10_1007_s43937_024_00027_7 crossref_primary_10_1016_j_jenvman_2022_117081 crossref_primary_10_1016_j_renene_2025_123820 crossref_primary_10_1007_s11760_023_02607_x crossref_primary_10_1002_ese3_1656 crossref_primary_10_1016_j_jpowsour_2025_236928 crossref_primary_10_1007_s44196_025_00759_x crossref_primary_10_1007_s11831_022_09804_w crossref_primary_10_1016_j_geits_2023_100108 crossref_primary_10_1016_j_est_2023_107868 crossref_primary_10_3390_app14135662 crossref_primary_10_3390_en18164416 crossref_primary_10_1021_acsnano_5c04200 crossref_primary_10_3389_fenrg_2022_972437 crossref_primary_10_1016_j_jpowsour_2024_234674 crossref_primary_10_1016_j_est_2023_109884 crossref_primary_10_1016_j_electacta_2024_144146 crossref_primary_10_1016_j_energy_2024_133978 crossref_primary_10_1007_s10586_024_04950_1 crossref_primary_10_1007_s10800_025_02262_9 crossref_primary_10_1016_j_eswa_2023_121904 crossref_primary_10_1109_MSMC_2023_3296932 crossref_primary_10_1016_j_isci_2024_109040 crossref_primary_10_1016_j_apenergy_2023_122417 crossref_primary_10_1007_s11581_024_05573_7 crossref_primary_10_1016_j_advengsoft_2024_103635 crossref_primary_10_3390_electronics13081423 crossref_primary_10_1016_j_energy_2022_124957 crossref_primary_10_1016_j_energy_2023_128739 |
| Cites_doi | 10.1039/C7CS00889A 10.1002/er.5934 10.3390/en10122012 10.1049/cje.2020.10.012 10.1016/j.est.2021.103076 10.3390/electronics10121497 10.3390/en12122247 10.1016/j.neucom.2019.03.084 10.1109/TIM.2008.2005965 10.1007/s10489-018-1334-8 10.1016/j.applthermaleng.2018.02.046 10.1016/j.ins.2017.02.026 10.1016/j.jpowsour.2019.227425 10.1142/S1469026817500122 10.32604/cmc.2020.013458 10.1016/j.est.2020.101741 10.1016/j.apenergy.2017.05.124 10.1109/JSEE.2015.00037 10.1109/ACCESS.2019.2947843 10.1016/j.jpowsour.2015.08.091 10.1039/C8TA10513H 10.1016/j.jpowsour.2019.227401 10.1007/s00521-013-1522-8 10.1016/j.asoc.2007.07.002 10.1080/21642583.2019.1708830 10.1016/j.jpowsour.2020.228863 10.3390/en13020375 10.1149/1945-7111/abc207 10.3390/en12224338 10.1016/j.neucom.2015.11.009 10.1016/j.jpowsour.2017.05.004 10.1016/j.neucom.2016.03.112 |
| ContentType | Journal Article |
| Copyright | 2022 The Authors 2022 The Authors. 2022 The Authors 2022 |
| Copyright_xml | – notice: 2022 The Authors – notice: 2022 The Authors. – notice: 2022 The Authors 2022 |
| DBID | 6I. AAFTH AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.1016/j.isci.2022.103988 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2589-0042 |
| ExternalDocumentID | oai_doaj_org_article_a8cfd42b5f2b477d9bc08d8fe376b4e6 PMC8927926 35310948 10_1016_j_isci_2022_103988 S2589004222002589 |
| Genre | Journal Article |
| GroupedDBID | 0SF 53G 6I. AACTN AAEDW AAFTH AALRI AAXUO ABMAC ADBBV AEXQZ AFTJW AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ AOIJS BCNDV EBS FDB GROUPED_DOAJ HYE M41 NCXOZ OK1 ROL RPM SSZ 0R~ AAMRU AAYWO AAYXX ACVFH ADCNI ADVLN AEUPX AFPUW AIGII AKBMS AKYEP APXCP CITATION EJD NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c521t-c7f179ff32a1c124a17d87a3ecd4ac1125a40ad666fd1121872d7f177cc7912f3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 57 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000787731400005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2589-0042 |
| IngestDate | Fri Oct 03 12:44:08 EDT 2025 Tue Sep 30 16:47:05 EDT 2025 Thu Jul 10 22:26:40 EDT 2025 Thu Jan 02 22:54:35 EST 2025 Thu Nov 13 04:35:47 EST 2025 Tue Nov 18 22:11:29 EST 2025 Tue Jul 25 20:59:21 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Energy management Machine learning |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. 2022 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c521t-c7f179ff32a1c124a17d87a3ecd4ac1125a40ad666fd1121872d7f177cc7912f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Lead contact |
| ORCID | 0000-0001-6117-6649 0000-0003-0302-4123 |
| OpenAccessLink | https://doaj.org/article/a8cfd42b5f2b477d9bc08d8fe376b4e6 |
| PMID | 35310948 |
| PQID | 2641517609 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_a8cfd42b5f2b477d9bc08d8fe376b4e6 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8927926 proquest_miscellaneous_2641517609 pubmed_primary_35310948 crossref_citationtrail_10_1016_j_isci_2022_103988 crossref_primary_10_1016_j_isci_2022_103988 elsevier_sciencedirect_doi_10_1016_j_isci_2022_103988 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-04-15 |
| PublicationDateYYYYMMDD | 2022-04-15 |
| PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | iScience |
| PublicationTitleAlternate | iScience |
| PublicationYear | 2022 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
| References | Xue, Shen (bib33) 2020; 8 Tubishat, Abushariah, Idris, Aljarah (bib29) 2019; 49 Fan, Xiao, Li, Yang, Tang (bib6) 2020; 32 Richardson, Osborne, Howey (bib21) 2017; 357 Salinas, Kowal (bib24) 2020; 167 Ding, Xu, Nie (bib4) 2014; 25 Li, Sengupta, Dechent, Howey, Annaswamy, Sauer (bib15) 2021; 482 Pang, Huang, Wen, Shi, Jia, Zeng (bib19) 2019; 12 Rosa Palacin (bib22) 2018; 47 Saha, Goebel, Poll, Christophersen (bib23) 2009; 58 Panchal, Mathew, Fraser, Fowler (bib18) 2018; 135 Sepasi, Ghorbani, Liaw (bib25) 2015; 299 Yu, Mo, Tang, Yang, Wan, Liu (bib35) 2017; 10 Xiong, Tian, Mu, Wang (bib32) 2017; 207 Tizhoosh (bib28) 2005 Bole, Kulkarni, Daigle (bib2) 2014 Xie, Ren, Yang, Wang, Sun, Chen, He (bib31) 2020; 448 Venugopal, Vigneswaran (bib30) 2019; 12 Fan, Fan, Liu, Qu, Li (bib5) 2019; 7 Kim, Song, Son, Ono, Qi (bib12) 2019; 7 Luo, Zhang, Liu, Guo, Zheng (bib16) 2017; 261 Mu, Zeng (bib17) 2019; 13 Qu, Lang, Liang, Qin, Crisalle (bib20) 2016; 175 Tian, Qin (bib27) 2021; 45 Tang, Deng, Huang (bib26) 2017 Kao, Zahara (bib11) 2008; 8 Li, Xiong, Li, Su, Wu (bib14) 2019; 350 Hussain, Khan, Abbas, Naqvi, Mushtaq, Rehman, Nadeem (bib8) 2021; 66 Bole, Kulkarni, Daigle (bib3) 2014 Jia, Liang, Shi, Wen, Pang, Zeng (bib9) 2020; 13 Zhang, Luo, Zhou (bib36) 2017; 11 Bian, Liu, Yan, Zou, Zhao (bib1) 2020; 448 Jia, Wang, Shi, Wen, Pang, Zeng (bib38) 2021; 42 Huang, Zhou, Wu, Luo (bib7) 2016; 25 Yang, Wen, Shi, Zeng (bib34) 2021; 10 Li, Zhang, Lai, Zhou, Xu (bib13) 2017; 396 Jia, Wang, Pang, Shi, Wen, Zeng (bib10) 2021; 30 Zhu, Xu, Li, Wu, Liu (bib37) 2015; 26 Ding (10.1016/j.isci.2022.103988_bib4) 2014; 25 Richardson (10.1016/j.isci.2022.103988_bib21) 2017; 357 Panchal (10.1016/j.isci.2022.103988_bib18) 2018; 135 Mu (10.1016/j.isci.2022.103988_bib17) 2019; 13 Kao (10.1016/j.isci.2022.103988_bib11) 2008; 8 Xue (10.1016/j.isci.2022.103988_bib33) 2020; 8 Fan (10.1016/j.isci.2022.103988_bib5) 2019; 7 Tizhoosh (10.1016/j.isci.2022.103988_bib28) 2005 Pang (10.1016/j.isci.2022.103988_bib19) 2019; 12 Saha (10.1016/j.isci.2022.103988_bib23) 2009; 58 Bole (10.1016/j.isci.2022.103988_bib3) 2014 Huang (10.1016/j.isci.2022.103988_bib7) 2016; 25 Kim (10.1016/j.isci.2022.103988_bib12) 2019; 7 Salinas (10.1016/j.isci.2022.103988_bib24) 2020; 167 Tubishat (10.1016/j.isci.2022.103988_bib29) 2019; 49 Xie (10.1016/j.isci.2022.103988_bib31) 2020; 448 Rosa Palacin (10.1016/j.isci.2022.103988_bib22) 2018; 47 Tian (10.1016/j.isci.2022.103988_bib27) 2021; 45 Li (10.1016/j.isci.2022.103988_bib13) 2017; 396 Li (10.1016/j.isci.2022.103988_bib14) 2019; 350 Yang (10.1016/j.isci.2022.103988_bib34) 2021; 10 Venugopal (10.1016/j.isci.2022.103988_bib30) 2019; 12 Yu (10.1016/j.isci.2022.103988_bib35) 2017; 10 Jia (10.1016/j.isci.2022.103988_bib10) 2021; 30 Sepasi (10.1016/j.isci.2022.103988_bib25) 2015; 299 Jia (10.1016/j.isci.2022.103988_bib9) 2020; 13 Zhu (10.1016/j.isci.2022.103988_bib37) 2015; 26 Bian (10.1016/j.isci.2022.103988_bib1) 2020; 448 Luo (10.1016/j.isci.2022.103988_bib16) 2017; 261 Zhang (10.1016/j.isci.2022.103988_bib36) 2017; 11 Bole (10.1016/j.isci.2022.103988_bib2) 2014 Fan (10.1016/j.isci.2022.103988_bib6) 2020; 32 Jia (10.1016/j.isci.2022.103988_bib38) 2021; 42 Xiong (10.1016/j.isci.2022.103988_bib32) 2017; 207 Hussain (10.1016/j.isci.2022.103988_bib8) 2021; 66 Li (10.1016/j.isci.2022.103988_bib15) 2021; 482 Qu (10.1016/j.isci.2022.103988_bib20) 2016; 175 Tang (10.1016/j.isci.2022.103988_bib26) 2017 |
| References_xml | – volume: 32 start-page: 101741 year: 2020 ident: bib6 article-title: A novel deep learning framework for state of health estimation of lithium-ion battery publication-title: J. Energ. Storage – volume: 45 start-page: 2383 year: 2021 end-page: 2397 ident: bib27 article-title: State of health prediction for lithium-ion batteries with a novel online sequential extreme learning machine method publication-title: Int. J. Energ. Res. – volume: 49 start-page: 1688 year: 2019 end-page: 1707 ident: bib29 article-title: Improved whale optimization algorithm for feature selection in Arabic sentiment analysis publication-title: Appl. Intelligence – volume: 167 start-page: 140519 year: 2020 ident: bib24 article-title: Discharge rate capability in aged Li-ion batteries publication-title: J. Electrochem. Soc. – volume: 299 start-page: 246 year: 2015 end-page: 254 ident: bib25 article-title: Inline state of health estimation of lithium-ion batteries using state of charge calculation publication-title: J. Power Sourc. – volume: 261 start-page: 164 year: 2017 end-page: 170 ident: bib16 article-title: Distributed extreme learning machine with alternating direction method of multiplier publication-title: Neurocomputing – volume: 13 start-page: 1738 year: 2019 end-page: 1764 ident: bib17 article-title: A review of deep learning research publication-title: Ksii Trans. Internet Inf. Syst. – volume: 135 start-page: 123 year: 2018 end-page: 132 ident: bib18 article-title: Electrochemical thermal modeling and experimental measurements of 18650 cylindrical lithium-ion battery during discharge cycle for an EV publication-title: Appl. Therm. Eng. – start-page: 809 year: 2017 end-page: 821 ident: bib26 article-title: Extreme Learning Machine for Multilayer Perceptron – volume: 10 start-page: 2012 year: 2017 ident: bib35 article-title: Indirect state-of-health estimation for lithium-ion batteries under randomized use publication-title: Energies – volume: 448 start-page: 227425 year: 2020 ident: bib31 article-title: Influence of cycling aging and ambient pressure on the thermal safety features of lithium-ion battery publication-title: J. Power Sourc. – volume: 350 start-page: 261 year: 2019 end-page: 270 ident: bib14 article-title: A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine publication-title: Neurocomputing – volume: 25 start-page: 567 year: 2016 end-page: 593 ident: bib7 article-title: A cuckoo search algorithm with elite opposition-based strategy publication-title: J. Intell. Syst. – volume: 11 start-page: 1750012 year: 2017 ident: bib36 article-title: Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method publication-title: Int. J. Comput. Intelligence Appl. – volume: 175 start-page: 826 year: 2016 end-page: 834 ident: bib20 article-title: Two-hidden-layer extreme learning machine for regression and classification publication-title: Neurocomputing – volume: 448 start-page: 227401 year: 2020 ident: bib1 article-title: An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: model development and validation publication-title: J. Power Sourc. – volume: 25 start-page: 549 year: 2014 end-page: 556 ident: bib4 article-title: Extreme learning machine and its applications publication-title: Neural Comput. Appl. – year: 2014 ident: bib3 article-title: Randomized Battery Usage Data Set. NASA Ames Prognostics Data Repository – volume: 207 start-page: 372 year: 2017 end-page: 383 ident: bib32 article-title: A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries publication-title: Appl. Energ. – volume: 8 start-page: 22 year: 2020 end-page: 34 ident: bib33 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst. Sci. Control Eng. – start-page: 695 year: 2005 end-page: 701 ident: bib28 article-title: Opposition-based learning: a new scheme for machine intelligence publication-title: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA '05) and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC '05) – volume: 26 start-page: 317 year: 2015 end-page: 328 ident: bib37 article-title: Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC publication-title: J. Syst. Eng. Electronics – volume: 66 start-page: 141 year: 2021 end-page: 156 ident: bib8 article-title: Enabling smart cities with cognition based intelligent route decision in vehicles empowered with deep extreme learning machine publication-title: Cmc-Computers Mater. Continua – volume: 12 start-page: 2247 year: 2019 ident: bib19 article-title: A lithium-ion battery RUL prediction method considering the capacity regeneration phenomenon publication-title: Energies – volume: 13 start-page: 375 year: 2020 ident: bib9 article-title: SOH and RUL prediction of lithium-ion batteries based on Gaussian process regression with indirect health indicators publication-title: Energies – volume: 12 start-page: 4338 year: 2019 ident: bib30 article-title: State-of-Health estimation of Li-ion batteries in electric vehicle using IndRNN under variable load condition publication-title: Energies – volume: 7 start-page: 160043 year: 2019 end-page: 160061 ident: bib5 article-title: A novel machine learning method based approach for Li-ion battery prognostic and health management publication-title: IEEE Access – volume: 396 start-page: 162 year: 2017 end-page: 181 ident: bib13 article-title: Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation publication-title: Inf. Sci. – volume: 8 start-page: 849 year: 2008 end-page: 857 ident: bib11 article-title: A hybrid genetic algorithm and particle swarm optimization for multimodal functions publication-title: Appl. Soft Comput. – volume: 47 start-page: 4924 year: 2018 end-page: 4933 ident: bib22 article-title: Understanding ageing in Li-ion batteries: a chemical issue publication-title: Chem. Soc. Rev. – volume: 58 start-page: 291 year: 2009 end-page: 296 ident: bib23 article-title: Prognostics methods for battery health monitoring using a Bayesian framework publication-title: IEEE Trans. Instrumentation Meas. – volume: 30 start-page: 26 year: 2021 end-page: 35 ident: bib10 article-title: Multi-scale prediction of RUL and SOH for lithium-ion batteries based on WNN-UPF combined model publication-title: Chin. J. Electronics – volume: 7 start-page: 2942 year: 2019 end-page: 2964 ident: bib12 article-title: Lithium-ion batteries: outlook on present, future, and hybridized technologies publication-title: J. Mater. Chem. A – volume: 357 start-page: 209 year: 2017 end-page: 219 ident: bib21 article-title: Gaussian process regression for forecasting battery state of health publication-title: J. Power Sourc. – volume: 10 start-page: 1497 year: 2021 ident: bib34 article-title: State of health prediction of lithium-ion batteries based on the discharge voltage and temperature publication-title: Electronics – year: 2014 ident: bib2 article-title: Adaptation of an Electrochemistry-Based Li-Ion Battery Model to Account for Deterioration Observed under Randomized Use – volume: 42 start-page: 103076 year: 2021 ident: bib38 article-title: A multi-scale state of health prediction framework of lithium-ion batteries considering the temperature variation during battery discharge publication-title: Journal of Energy Storage – volume: 482 start-page: 228863 year: 2021 ident: bib15 article-title: Online capacity estimation of lithium-ion batteries with deep long short-term memory networks publication-title: J. Power Sourc. – volume: 47 start-page: 4924 year: 2018 ident: 10.1016/j.isci.2022.103988_bib22 article-title: Understanding ageing in Li-ion batteries: a chemical issue publication-title: Chem. Soc. Rev. doi: 10.1039/C7CS00889A – volume: 45 start-page: 2383 year: 2021 ident: 10.1016/j.isci.2022.103988_bib27 article-title: State of health prediction for lithium-ion batteries with a novel online sequential extreme learning machine method publication-title: Int. J. Energ. Res. doi: 10.1002/er.5934 – volume: 10 start-page: 2012 year: 2017 ident: 10.1016/j.isci.2022.103988_bib35 article-title: Indirect state-of-health estimation for lithium-ion batteries under randomized use publication-title: Energies doi: 10.3390/en10122012 – volume: 30 start-page: 26 year: 2021 ident: 10.1016/j.isci.2022.103988_bib10 article-title: Multi-scale prediction of RUL and SOH for lithium-ion batteries based on WNN-UPF combined model publication-title: Chin. J. Electronics doi: 10.1049/cje.2020.10.012 – volume: 42 start-page: 103076 year: 2021 ident: 10.1016/j.isci.2022.103988_bib38 article-title: A multi-scale state of health prediction framework of lithium-ion batteries considering the temperature variation during battery discharge publication-title: Journal of Energy Storage doi: 10.1016/j.est.2021.103076 – volume: 10 start-page: 1497 year: 2021 ident: 10.1016/j.isci.2022.103988_bib34 article-title: State of health prediction of lithium-ion batteries based on the discharge voltage and temperature publication-title: Electronics doi: 10.3390/electronics10121497 – volume: 12 start-page: 2247 year: 2019 ident: 10.1016/j.isci.2022.103988_bib19 article-title: A lithium-ion battery RUL prediction method considering the capacity regeneration phenomenon publication-title: Energies doi: 10.3390/en12122247 – volume: 350 start-page: 261 year: 2019 ident: 10.1016/j.isci.2022.103988_bib14 article-title: A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.03.084 – volume: 58 start-page: 291 year: 2009 ident: 10.1016/j.isci.2022.103988_bib23 article-title: Prognostics methods for battery health monitoring using a Bayesian framework publication-title: IEEE Trans. Instrumentation Meas. doi: 10.1109/TIM.2008.2005965 – volume: 49 start-page: 1688 year: 2019 ident: 10.1016/j.isci.2022.103988_bib29 article-title: Improved whale optimization algorithm for feature selection in Arabic sentiment analysis publication-title: Appl. Intelligence doi: 10.1007/s10489-018-1334-8 – volume: 135 start-page: 123 year: 2018 ident: 10.1016/j.isci.2022.103988_bib18 article-title: Electrochemical thermal modeling and experimental measurements of 18650 cylindrical lithium-ion battery during discharge cycle for an EV publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2018.02.046 – volume: 396 start-page: 162 year: 2017 ident: 10.1016/j.isci.2022.103988_bib13 article-title: Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.02.026 – volume: 448 start-page: 227425 year: 2020 ident: 10.1016/j.isci.2022.103988_bib31 article-title: Influence of cycling aging and ambient pressure on the thermal safety features of lithium-ion battery publication-title: J. Power Sourc. doi: 10.1016/j.jpowsour.2019.227425 – volume: 11 start-page: 1750012 year: 2017 ident: 10.1016/j.isci.2022.103988_bib36 article-title: Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method publication-title: Int. J. Comput. Intelligence Appl. doi: 10.1142/S1469026817500122 – volume: 66 start-page: 141 year: 2021 ident: 10.1016/j.isci.2022.103988_bib8 article-title: Enabling smart cities with cognition based intelligent route decision in vehicles empowered with deep extreme learning machine publication-title: Cmc-Computers Mater. Continua doi: 10.32604/cmc.2020.013458 – year: 2014 ident: 10.1016/j.isci.2022.103988_bib3 – volume: 32 start-page: 101741 year: 2020 ident: 10.1016/j.isci.2022.103988_bib6 article-title: A novel deep learning framework for state of health estimation of lithium-ion battery publication-title: J. Energ. Storage doi: 10.1016/j.est.2020.101741 – volume: 207 start-page: 372 year: 2017 ident: 10.1016/j.isci.2022.103988_bib32 article-title: A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries publication-title: Appl. Energ. doi: 10.1016/j.apenergy.2017.05.124 – volume: 25 start-page: 567 year: 2016 ident: 10.1016/j.isci.2022.103988_bib7 article-title: A cuckoo search algorithm with elite opposition-based strategy publication-title: J. Intell. Syst. – volume: 26 start-page: 317 year: 2015 ident: 10.1016/j.isci.2022.103988_bib37 article-title: Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC publication-title: J. Syst. Eng. Electronics doi: 10.1109/JSEE.2015.00037 – volume: 7 start-page: 160043 year: 2019 ident: 10.1016/j.isci.2022.103988_bib5 article-title: A novel machine learning method based approach for Li-ion battery prognostic and health management publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2947843 – volume: 299 start-page: 246 year: 2015 ident: 10.1016/j.isci.2022.103988_bib25 article-title: Inline state of health estimation of lithium-ion batteries using state of charge calculation publication-title: J. Power Sourc. doi: 10.1016/j.jpowsour.2015.08.091 – start-page: 695 year: 2005 ident: 10.1016/j.isci.2022.103988_bib28 article-title: Opposition-based learning: a new scheme for machine intelligence – volume: 7 start-page: 2942 year: 2019 ident: 10.1016/j.isci.2022.103988_bib12 article-title: Lithium-ion batteries: outlook on present, future, and hybridized technologies publication-title: J. Mater. Chem. A doi: 10.1039/C8TA10513H – volume: 13 start-page: 1738 year: 2019 ident: 10.1016/j.isci.2022.103988_bib17 article-title: A review of deep learning research publication-title: Ksii Trans. Internet Inf. Syst. – volume: 448 start-page: 227401 year: 2020 ident: 10.1016/j.isci.2022.103988_bib1 article-title: An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: model development and validation publication-title: J. Power Sourc. doi: 10.1016/j.jpowsour.2019.227401 – volume: 25 start-page: 549 year: 2014 ident: 10.1016/j.isci.2022.103988_bib4 article-title: Extreme learning machine and its applications publication-title: Neural Comput. Appl. doi: 10.1007/s00521-013-1522-8 – volume: 8 start-page: 849 year: 2008 ident: 10.1016/j.isci.2022.103988_bib11 article-title: A hybrid genetic algorithm and particle swarm optimization for multimodal functions publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2007.07.002 – volume: 8 start-page: 22 year: 2020 ident: 10.1016/j.isci.2022.103988_bib33 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2019.1708830 – volume: 482 start-page: 228863 year: 2021 ident: 10.1016/j.isci.2022.103988_bib15 article-title: Online capacity estimation of lithium-ion batteries with deep long short-term memory networks publication-title: J. Power Sourc. doi: 10.1016/j.jpowsour.2020.228863 – start-page: 809 year: 2017 ident: 10.1016/j.isci.2022.103988_bib26 – volume: 13 start-page: 375 year: 2020 ident: 10.1016/j.isci.2022.103988_bib9 article-title: SOH and RUL prediction of lithium-ion batteries based on Gaussian process regression with indirect health indicators publication-title: Energies doi: 10.3390/en13020375 – volume: 167 start-page: 140519 year: 2020 ident: 10.1016/j.isci.2022.103988_bib24 article-title: Discharge rate capability in aged Li-ion batteries publication-title: J. Electrochem. Soc. doi: 10.1149/1945-7111/abc207 – volume: 12 start-page: 4338 year: 2019 ident: 10.1016/j.isci.2022.103988_bib30 article-title: State-of-Health estimation of Li-ion batteries in electric vehicle using IndRNN under variable load condition publication-title: Energies doi: 10.3390/en12224338 – year: 2014 ident: 10.1016/j.isci.2022.103988_bib2 – volume: 175 start-page: 826 year: 2016 ident: 10.1016/j.isci.2022.103988_bib20 article-title: Two-hidden-layer extreme learning machine for regression and classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.11.009 – volume: 357 start-page: 209 year: 2017 ident: 10.1016/j.isci.2022.103988_bib21 article-title: Gaussian process regression for forecasting battery state of health publication-title: J. Power Sourc. doi: 10.1016/j.jpowsour.2017.05.004 – volume: 261 start-page: 164 year: 2017 ident: 10.1016/j.isci.2022.103988_bib16 article-title: Distributed extreme learning machine with alternating direction method of multiplier publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.03.112 |
| SSID | ssj0002002496 |
| Score | 2.462926 |
| Snippet | Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation... |
| SourceID | doaj pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 103988 |
| SubjectTerms | Energy management Machine learning |
| Title | Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction |
| URI | https://dx.doi.org/10.1016/j.isci.2022.103988 https://www.ncbi.nlm.nih.gov/pubmed/35310948 https://www.proquest.com/docview/2641517609 https://pubmed.ncbi.nlm.nih.gov/PMC8927926 https://doaj.org/article/a8cfd42b5f2b477d9bc08d8fe376b4e6 |
| Volume | 25 |
| WOSCitedRecordID | wos000787731400005&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2589-0042 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002002496 issn: 2589-0042 databaseCode: DOA dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZgxYELAvHqAisjcUMReTgZ-wiIFacVB5B6s_zcZrVNqm6LxIm_zoydlhak5cIxiZ3Enhn7sz3zDWNvrFONMwElEEQohAW0ORks2pUpo6jBgszJJuDiQs7n6stBqi_yCcv0wLnj3hnpohe1bWNtBYBX1pXSyxjQMqwIiWy7BHWwmLpKx2tEhZcyy7XkE4SqOUXMZOcuinjFxWFdU9C5SmlXfs9Kibz_aHL6G3z-6UN5MCmdP2QPJjTJ3-dWPGJ3wvCY_cwbBcFzHC2IYpFndebm-nJc95vFko84UCynCEzuQ1hxHKNpp5BPWSQu-TJ5WQaOoJYjVF_022VBpW0i5PzBUyRSMcYiR1Ly1ZqOfOiFT9i3809fP34upjwLhaN0BoWDiGYZY1ObyuF8byrwEkwTnBfGISBrjSiNx4VO9HhVSag9VQHnQFV1bJ6yk2EcwnPGa6sCrRCrzoPoOm9aZ4yTEIWzANLPWLXrZ-0mEnLKhXGtd95mV5pko0k2Ostmxt7u66wyBcetpT-Q-PYliT473UCl0pNS6X8p1Yy1O-HrCYlkhIGv6m_9-Oudpmg0Uzp7MUMYtzcacSdiK-hKNWPPsubsf7FpiZ5VYG040qmjNhw_GfpFogKXigggu9P_0egX7D41hY7KqvYlO9mst-EVu-e-b_qb9Rm7C3N5lqzsF1-ZL6g |
| linkProvider | Directory of Open Access Journals |
| 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=Improved+sparrow+search+algorithm+optimization+deep+extreme+learning+machine+for+lithium-ion+battery+state-of-health+prediction&rft.jtitle=iScience&rft.au=Jia%2C+Jianfang&rft.au=Yuan%2C+Shufang&rft.au=Shi%2C+Yuanhao&rft.au=Wen%2C+Jie&rft.date=2022-04-15&rft.issn=2589-0042&rft.eissn=2589-0042&rft.volume=25&rft.issue=4&rft.spage=103988&rft_id=info:doi/10.1016%2Fj.isci.2022.103988&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_isci_2022_103988 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2589-0042&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2589-0042&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2589-0042&client=summon |