A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm
An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack...
Gespeichert in:
| Veröffentlicht in: | Journal of power sources Jg. 376; S. 191 - 199 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.02.2018
|
| Schlagworte: | |
| ISSN: | 0378-7753, 1873-2755 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.
•A novel battery pack SOH definition is proposed.•A PSO-GA estimator is applied in parameters identification.•The accuracy and robustness of the method is verified by different profiles.•The influential battery pack SOH factors are performed. |
|---|---|
| AbstractList | An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.
•A novel battery pack SOH definition is proposed.•A PSO-GA estimator is applied in parameters identification.•The accuracy and robustness of the method is verified by different profiles.•The influential battery pack SOH factors are performed. |
| Author | Chen, Zonghai Wang, Yujie Zhang, Xu Liu, Chang |
| Author_xml | – sequence: 1 givenname: Xu surname: Zhang fullname: Zhang, Xu – sequence: 2 givenname: Yujie surname: Wang fullname: Wang, Yujie – sequence: 3 givenname: Chang orcidid: 0000-0002-7292-6107 surname: Liu fullname: Liu, Chang – sequence: 4 givenname: Zonghai orcidid: 0000-0001-9312-9089 surname: Chen fullname: Chen, Zonghai email: chenzh@ustc.edu.cn |
| BookMark | eNqFkF1LwzAUhoNMcJv-BckfaE2apmnBC8fwCwbe6HVI09M1tWtKkinz19s6vfFmVwcO53k577NAs972gNA1JTElNLtp43awn97uXZwQKmJKY5LlZ2hOc8GiRHA-Q3PCRB4JwdkFWnjfEkIoFWSOhhXu7Qd0WA2Ds0o32Na4VCGAO-BB6XfsgwowbRtQXWgw-GB2Khjb4703_RYrF0xttFEdNn2ArjNb6PWIDOOh-Tqeqm5rnQnN7hKd16rzcPU7l-jt4f51_RRtXh6f16tNpFMqQkTLshjfV5zzmlW80FWaVIXIWKlVApwVnOc1ZyQtBeNVDQCJSgtO9FgroblmS3R7zNXOeu-gltqEn1-CU6aTlMjJnmzlnz052ZOUytHeiGf_8MGNtd3hNHh3BGEs92HASa_N5KMyDnSQlTWnIr4BGhGUNA |
| CitedBy_id | crossref_primary_10_1016_j_energy_2024_134258 crossref_primary_10_3390_electronics11111789 crossref_primary_10_1155_2021_7049857 crossref_primary_10_3390_batteries9090437 crossref_primary_10_1007_s10479_020_03856_6 crossref_primary_10_1007_s42154_020_00128_8 crossref_primary_10_1016_j_cirpj_2021_02_004 crossref_primary_10_1016_j_energy_2024_130656 crossref_primary_10_1155_2024_6488186 crossref_primary_10_3390_en12214031 crossref_primary_10_1109_ACCESS_2019_2891063 crossref_primary_10_3390_electronics10131588 crossref_primary_10_1016_j_est_2023_107083 crossref_primary_10_1007_s11227_024_06092_y crossref_primary_10_1109_TVT_2019_2932605 crossref_primary_10_1016_j_est_2023_109148 crossref_primary_10_1109_ACCESS_2019_2943558 crossref_primary_10_3390_en12244772 crossref_primary_10_1155_2022_6842974 crossref_primary_10_1016_j_est_2018_04_020 crossref_primary_10_1002_ente_202400488 crossref_primary_10_4018_JOEUC_336277 crossref_primary_10_1016_j_etran_2020_100064 crossref_primary_10_1109_TVT_2021_3125194 crossref_primary_10_1016_j_est_2022_105752 crossref_primary_10_1016_j_est_2021_103591 crossref_primary_10_1016_j_apenergy_2018_02_117 crossref_primary_10_1016_j_apenergy_2020_114569 crossref_primary_10_1016_j_ijhydene_2023_03_194 crossref_primary_10_1109_ACCESS_2019_2948291 crossref_primary_10_3390_electronics11172695 crossref_primary_10_1016_j_energy_2022_124851 crossref_primary_10_1016_j_fuel_2022_126862 crossref_primary_10_1016_j_energy_2023_126855 crossref_primary_10_1016_j_jpowsour_2019_05_092 crossref_primary_10_1016_j_asoc_2022_109688 crossref_primary_10_1016_j_energy_2021_120235 crossref_primary_10_1109_TTE_2021_3115597 crossref_primary_10_1016_j_energy_2019_04_070 crossref_primary_10_3390_en12060987 crossref_primary_10_1016_j_est_2023_107159 crossref_primary_10_1016_j_jpowsour_2022_231733 crossref_primary_10_1109_ACCESS_2021_3058018 crossref_primary_10_3390_wevj12030113 crossref_primary_10_1016_j_memsci_2020_118668 crossref_primary_10_1016_j_est_2022_106206 crossref_primary_10_1109_TIM_2019_2910919 crossref_primary_10_3390_wevj14090247 crossref_primary_10_1016_j_ress_2018_11_013 crossref_primary_10_1016_j_energy_2018_10_131 crossref_primary_10_1080_1206212X_2019_1639353 crossref_primary_10_1016_j_eswa_2022_117192 crossref_primary_10_1016_j_jiec_2018_11_034 crossref_primary_10_1016_j_apenergy_2020_115504 crossref_primary_10_1016_j_joule_2019_11_018 crossref_primary_10_1063_5_0092074 crossref_primary_10_1016_j_jclepro_2021_128015 crossref_primary_10_1049_enc2_12125 crossref_primary_10_1016_j_applthermaleng_2021_117088 crossref_primary_10_1016_j_energy_2024_132856 crossref_primary_10_1016_j_est_2024_114711 crossref_primary_10_1088_1742_6596_1757_1_012015 crossref_primary_10_1088_1742_6596_1757_1_012014 crossref_primary_10_3390_en18143750 crossref_primary_10_3390_en17092145 crossref_primary_10_3390_batteries10110394 crossref_primary_10_1109_TIE_2021_3108715 crossref_primary_10_1016_j_est_2023_107102 crossref_primary_10_1016_j_measurement_2019_06_052 crossref_primary_10_1049_ell2_12523 crossref_primary_10_1016_j_heliyon_2024_e38985 crossref_primary_10_3390_en15165981 crossref_primary_10_1016_j_energy_2022_123537 crossref_primary_10_1109_TTE_2023_3274819 crossref_primary_10_1007_s43236_020_00122_7 crossref_primary_10_1007_s43236_025_01072_8 crossref_primary_10_3389_fphy_2023_1161977 crossref_primary_10_1016_j_est_2023_106604 crossref_primary_10_1016_j_energy_2021_119901 crossref_primary_10_1016_j_energy_2019_07_127 crossref_primary_10_3390_en16248088 crossref_primary_10_1051_e3sconf_202560100071 crossref_primary_10_1088_1755_1315_645_1_012006 crossref_primary_10_1016_j_est_2024_112703 crossref_primary_10_1155_2021_7480599 crossref_primary_10_1109_ACCESS_2019_2930680 crossref_primary_10_1016_j_est_2024_114446 crossref_primary_10_1016_j_eswa_2023_121904 crossref_primary_10_1002_er_7360 crossref_primary_10_1016_j_est_2023_107493 crossref_primary_10_1109_TIE_2020_3045745 crossref_primary_10_1016_j_electacta_2020_136576 |
| Cites_doi | 10.1007/s10008-015-2910-z 10.1016/j.jpowsour.2014.06.111 10.1016/j.jpowsour.2016.11.104 10.1016/j.jpowsour.2016.10.040 10.1016/j.jpowsour.2015.11.087 10.1021/acsami.5b02729 10.1016/j.jpowsour.2015.12.001 10.1016/j.jpowsour.2013.01.018 10.1016/j.autcon.2016.08.004 10.1016/j.carbon.2016.06.076 10.1016/j.apenergy.2008.11.021 10.1016/j.jpowsour.2016.07.065 10.1016/j.jpowsour.2017.05.004 10.1016/j.jpowsour.2003.12.001 10.1016/j.enpol.2011.11.090 10.1016/j.ijepes.2012.04.050 10.1016/j.jpowsour.2013.10.114 10.1109/TSMC.2013.2296276 10.1016/j.jpowsour.2004.02.032 10.1016/j.apenergy.2015.12.063 10.1109/TVT.2005.847211 10.1016/j.egypro.2017.03.673 10.1016/j.jpowsour.2010.08.035 10.1109/TCAPT.2002.803653 10.1109/TVT.2016.2572163 10.1016/j.jpowsour.2017.01.054 10.1016/j.jpowsour.2012.10.060 10.1016/j.energy.2016.08.109 10.1016/j.apenergy.2015.03.110 |
| ContentType | Journal Article |
| Copyright | 2017 Elsevier B.V. |
| Copyright_xml | – notice: 2017 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.jpowsour.2017.11.068 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-2755 |
| EndPage | 199 |
| ExternalDocumentID | 10_1016_j_jpowsour_2017_11_068 S0378775317315434 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AABXZ AACTN AAEDT AAEDW AAEPC AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARJD AARLI AAXUO ABFNM ABMAC ABXRA ABYKQ ACDAQ ACGFS ACRLP ADBBV ADECG ADEZE AEBSH AEKER AENEX AEZYN AFKWA AFRZQ AFTJW AFZHZ AGHFR AGUBO AGYEJ AHHHB AHIDL AIEXJ AIKHN AITUG AJBFU AJOXV AJSZI ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FLBIZ FNPLU FYGXN G-Q GBLVA IHE J1W JARJE KOM LX7 LY6 M41 MAGPM MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SDF SDG SDP SES SPC SPCBC SSK SSM SSR SSZ T5K XPP ZMT ~G- 29L 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN BBWZM CITATION EFKBS FEDTE FGOYB G-2 HLY HVGLF HZ~ NDZJH R2- SAC SCB SCE SEW T9H VH1 VOH WUQ ~HD |
| ID | FETCH-LOGICAL-c417t-1bb9873a555f3d59cd42d9763bca2e539558f5304b735dfeee2a4950c117218c3 |
| ISICitedReferencesCount | 102 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000419810700024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0378-7753 |
| IngestDate | Sat Nov 29 02:55:44 EST 2025 Tue Nov 18 20:50:27 EST 2025 Fri Feb 23 02:28:11 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Particle swarm optimization-genetic algorithm Battery pack state of health Particle filter Battery pack model |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c417t-1bb9873a555f3d59cd42d9763bca2e539558f5304b735dfeee2a4950c117218c3 |
| ORCID | 0000-0002-7292-6107 0000-0001-9312-9089 |
| PageCount | 9 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_jpowsour_2017_11_068 crossref_primary_10_1016_j_jpowsour_2017_11_068 elsevier_sciencedirect_doi_10_1016_j_jpowsour_2017_11_068 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-02-01 |
| PublicationDateYYYYMMDD | 2018-02-01 |
| PublicationDate_xml | – month: 02 year: 2018 text: 2018-02-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Journal of power sources |
| PublicationYear | 2018 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Yang, Wang, Pan, Chen, Chen (bib13) 2017 Nik, Nejad, Zakeri (bib34) 2016; 71 Zenati, Desprez, Razik (bib19) 2010 Yuan, Dung (bib21) 2017; 66 Feng, Li, Ouyang, Lu, Li, He (bib24) 2013; 232 Plett (bib31) 2004; 134 Wang, Zhang, Chen (bib28) 2016; 305 Eddahech, Briat, Vinassa (bib17) 2012 Yu, Wei, Wang (bib33) 2012; 42 Nair, Destro, Bella, Appetecchi, Gerbaldi (bib5) 2016; 306 Nair, Porcarelli, Bella, Gerbaldi (bib3) 2015; 7 Zhang, Wang, Liu, Chen (bib27) 2017; 343 Richardson, Osborne, Howey (bib12) 2017; 357 Koot, Kessels, de Jager, Heemels, Van den Bosch, Steinbuch (bib25) 2005; 54 Leijen, Steyn-Ross, Kularatna (bib14) 2017 Zolin, Nair, Beneventi, Bella, Destro, Jagdale, Cannavaro, Tagliaferro, Chaussy, Geobaldo (bib1) 2016; 107 Zhang, Wang, Yang, Chen (bib30) 2016; 115 Ng, Moo, Chen, Hsieh (bib11) 2009; 86 Yang, Xu, Cao, Xu, Li, Wang (bib16) 2017; 105 Lu, Han, Li, Hua, Ouyang (bib2) 2013; 226 Radzir, Hanifah, Ahmad, Hassan, Bella (bib4) 2015; 19 Abu-Sharkh, Doerffel (bib32) 2004; 130 Ouyang, Feng, Han, Lu, Li, He (bib9) 2016; 165 Han, Ouyang, Lu, Li (bib7) 2014; 268 Zou, Manzie, Nešić, Kallapur (bib15) 2016; 335 Remmlinger, Buchholz, Meiler, Bernreuter, Dietmayer (bib18) 2011; 196 Kim, Qiao, Qu (bib10) 2013 Liu, Ouyang, Lu, Li, Hua (bib26) 2015; 149 Gao, Liu, Dougal (bib29) 2002; 25 Raghavan, Kiesel, Sommer, Schwartz, Lochbaum, Hegyi, Schuh, Arakaki, Saha, Ganguli (bib6) 2017; 341 Li, Pan, Chen (bib22) 2014; 44 Wu, Wang, Zhang, Chen (bib8) 2016; 327 Guo, Qiu, Hou, Liaw, Zhang (bib23) 2014; 249 Eddahech, Briat, Bertrand, Delétage, Vinassa (bib20) 2012; 42 Zolin (10.1016/j.jpowsour.2017.11.068_bib1) 2016; 107 Liu (10.1016/j.jpowsour.2017.11.068_bib26) 2015; 149 Han (10.1016/j.jpowsour.2017.11.068_bib7) 2014; 268 Koot (10.1016/j.jpowsour.2017.11.068_bib25) 2005; 54 Zou (10.1016/j.jpowsour.2017.11.068_bib15) 2016; 335 Wu (10.1016/j.jpowsour.2017.11.068_bib8) 2016; 327 Radzir (10.1016/j.jpowsour.2017.11.068_bib4) 2015; 19 Raghavan (10.1016/j.jpowsour.2017.11.068_bib6) 2017; 341 Richardson (10.1016/j.jpowsour.2017.11.068_bib12) 2017; 357 Yang (10.1016/j.jpowsour.2017.11.068_bib16) 2017; 105 Guo (10.1016/j.jpowsour.2017.11.068_bib23) 2014; 249 Gao (10.1016/j.jpowsour.2017.11.068_bib29) 2002; 25 Plett (10.1016/j.jpowsour.2017.11.068_bib31) 2004; 134 Leijen (10.1016/j.jpowsour.2017.11.068_bib14) 2017 Zhang (10.1016/j.jpowsour.2017.11.068_bib30) 2016; 115 Remmlinger (10.1016/j.jpowsour.2017.11.068_bib18) 2011; 196 Nair (10.1016/j.jpowsour.2017.11.068_bib5) 2016; 306 Abu-Sharkh (10.1016/j.jpowsour.2017.11.068_bib32) 2004; 130 Kim (10.1016/j.jpowsour.2017.11.068_bib10) 2013 Eddahech (10.1016/j.jpowsour.2017.11.068_bib17) 2012 Li (10.1016/j.jpowsour.2017.11.068_bib22) 2014; 44 Ng (10.1016/j.jpowsour.2017.11.068_bib11) 2009; 86 Eddahech (10.1016/j.jpowsour.2017.11.068_bib20) 2012; 42 Yang (10.1016/j.jpowsour.2017.11.068_bib13) 2017 Zhang (10.1016/j.jpowsour.2017.11.068_bib27) 2017; 343 Nair (10.1016/j.jpowsour.2017.11.068_bib3) 2015; 7 Yu (10.1016/j.jpowsour.2017.11.068_bib33) 2012; 42 Lu (10.1016/j.jpowsour.2017.11.068_bib2) 2013; 226 Zenati (10.1016/j.jpowsour.2017.11.068_bib19) 2010 Wang (10.1016/j.jpowsour.2017.11.068_bib28) 2016; 305 Nik (10.1016/j.jpowsour.2017.11.068_bib34) 2016; 71 Ouyang (10.1016/j.jpowsour.2017.11.068_bib9) 2016; 165 Yuan (10.1016/j.jpowsour.2017.11.068_bib21) 2017; 66 Feng (10.1016/j.jpowsour.2017.11.068_bib24) 2013; 232 |
| References_xml | – volume: 232 start-page: 209 year: 2013 end-page: 218 ident: bib24 publication-title: J. Power Sources – volume: 130 start-page: 266 year: 2004 end-page: 274 ident: bib32 publication-title: J. Power Sources – volume: 226 start-page: 272 year: 2013 end-page: 288 ident: bib2 publication-title: J. power sources – volume: 42 start-page: 487 year: 2012 end-page: 494 ident: bib20 publication-title: Int. J. Electr. Power & Energy Syst. – volume: 357 start-page: 209 year: 2017 end-page: 219 ident: bib12 publication-title: J. Power Sources – start-page: 4501 year: 2012 end-page: 4505 ident: bib17 publication-title: Energy Conversion Congress and Exposition (ECCE) – volume: 66 start-page: 2019 year: 2017 end-page: 2032 ident: bib21 publication-title: IEEE Trans. Veh. Technol. – volume: 19 start-page: 3079 year: 2015 end-page: 3085 ident: bib4 publication-title: J. Solid State Electrochem. – volume: 134 start-page: 262 year: 2004 end-page: 276 ident: bib31 publication-title: J. Power Sources – volume: 7 start-page: 12961 year: 2015 end-page: 12971 ident: bib3 publication-title: ACS Appl. Mater. interfaces – volume: 42 start-page: 329 year: 2012 end-page: 340 ident: bib33 publication-title: Energy Policy – volume: 335 start-page: 121 year: 2016 end-page: 130 ident: bib15 publication-title: J. Power Sources – volume: 115 start-page: 219 year: 2016 end-page: 229 ident: bib30 publication-title: Energy – volume: 306 start-page: 258 year: 2016 end-page: 267 ident: bib5 publication-title: J. Power Sources – volume: 165 start-page: 48 year: 2016 end-page: 59 ident: bib9 publication-title: Appl. Energy – volume: 327 start-page: 457 year: 2016 end-page: 464 ident: bib8 publication-title: J. Power Sources – start-page: 1773 year: 2010 end-page: 1778 ident: bib19 publication-title: IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society – volume: 341 start-page: 466 year: 2017 end-page: 473 ident: bib6 publication-title: J. Power Sources – volume: 268 start-page: 658 year: 2014 end-page: 669 ident: bib7 publication-title: J. Power Sources – volume: 54 start-page: 771 year: 2005 end-page: 782 ident: bib25 publication-title: IEEE Trans. Veh. Technol. – year: 2017 ident: bib13 publication-title: Appl. Energy – volume: 105 start-page: 2342 year: 2017 end-page: 2347 ident: bib16 publication-title: Energy Procedia – year: 2017 ident: bib14 publication-title: IEEE Trans. Veh. Technol. – volume: 71 start-page: 325 year: 2016 end-page: 345 ident: bib34 publication-title: Automation Constr. – volume: 249 start-page: 457 year: 2014 end-page: 462 ident: bib23 publication-title: J. Power Sources – volume: 149 start-page: 297 year: 2015 end-page: 314 ident: bib26 publication-title: Appl. Energy – volume: 343 start-page: 216 year: 2017 end-page: 225 ident: bib27 publication-title: J. Power Sources – volume: 107 start-page: 811 year: 2016 end-page: 822 ident: bib1 publication-title: Carbon – volume: 196 start-page: 5357 year: 2011 end-page: 5363 ident: bib18 publication-title: J. Power Sources – volume: 305 start-page: 80 year: 2016 end-page: 88 ident: bib28 publication-title: J. Power Sources – volume: 44 start-page: 851 year: 2014 end-page: 862 ident: bib22 publication-title: IEEE Trans. Syst. Man, Cybern. Syst. – start-page: 292 year: 2013 end-page: 298 ident: bib10 publication-title: Energy Conversion Congress and Exposition (ECCE) – volume: 86 start-page: 1506 year: 2009 end-page: 1511 ident: bib11 publication-title: Appl. energy – volume: 25 start-page: 495 year: 2002 end-page: 505 ident: bib29 publication-title: IEEE Trans. components Packag. Technol. – volume: 19 start-page: 3079 year: 2015 ident: 10.1016/j.jpowsour.2017.11.068_bib4 publication-title: J. Solid State Electrochem. doi: 10.1007/s10008-015-2910-z – start-page: 292 year: 2013 ident: 10.1016/j.jpowsour.2017.11.068_bib10 – volume: 268 start-page: 658 year: 2014 ident: 10.1016/j.jpowsour.2017.11.068_bib7 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2014.06.111 – volume: 341 start-page: 466 year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib6 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.11.104 – start-page: 4501 year: 2012 ident: 10.1016/j.jpowsour.2017.11.068_bib17 – volume: 335 start-page: 121 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib15 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.10.040 – volume: 305 start-page: 80 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib28 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2015.11.087 – volume: 7 start-page: 12961 year: 2015 ident: 10.1016/j.jpowsour.2017.11.068_bib3 publication-title: ACS Appl. Mater. interfaces doi: 10.1021/acsami.5b02729 – volume: 306 start-page: 258 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib5 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2015.12.001 – volume: 232 start-page: 209 year: 2013 ident: 10.1016/j.jpowsour.2017.11.068_bib24 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2013.01.018 – volume: 71 start-page: 325 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib34 publication-title: Automation Constr. doi: 10.1016/j.autcon.2016.08.004 – volume: 107 start-page: 811 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib1 publication-title: Carbon doi: 10.1016/j.carbon.2016.06.076 – volume: 86 start-page: 1506 year: 2009 ident: 10.1016/j.jpowsour.2017.11.068_bib11 publication-title: Appl. energy doi: 10.1016/j.apenergy.2008.11.021 – volume: 327 start-page: 457 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib8 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.07.065 – volume: 357 start-page: 209 year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib12 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2017.05.004 – volume: 130 start-page: 266 year: 2004 ident: 10.1016/j.jpowsour.2017.11.068_bib32 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2003.12.001 – volume: 42 start-page: 329 year: 2012 ident: 10.1016/j.jpowsour.2017.11.068_bib33 publication-title: Energy Policy doi: 10.1016/j.enpol.2011.11.090 – volume: 42 start-page: 487 year: 2012 ident: 10.1016/j.jpowsour.2017.11.068_bib20 publication-title: Int. J. Electr. Power & Energy Syst. doi: 10.1016/j.ijepes.2012.04.050 – volume: 249 start-page: 457 year: 2014 ident: 10.1016/j.jpowsour.2017.11.068_bib23 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2013.10.114 – volume: 44 start-page: 851 year: 2014 ident: 10.1016/j.jpowsour.2017.11.068_bib22 publication-title: IEEE Trans. Syst. Man, Cybern. Syst. doi: 10.1109/TSMC.2013.2296276 – volume: 134 start-page: 262 year: 2004 ident: 10.1016/j.jpowsour.2017.11.068_bib31 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2004.02.032 – volume: 165 start-page: 48 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib9 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.12.063 – volume: 54 start-page: 771 year: 2005 ident: 10.1016/j.jpowsour.2017.11.068_bib25 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2005.847211 – volume: 105 start-page: 2342 year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib16 publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.03.673 – start-page: 1773 year: 2010 ident: 10.1016/j.jpowsour.2017.11.068_bib19 – year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib14 publication-title: IEEE Trans. Veh. Technol. – volume: 196 start-page: 5357 year: 2011 ident: 10.1016/j.jpowsour.2017.11.068_bib18 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2010.08.035 – volume: 25 start-page: 495 year: 2002 ident: 10.1016/j.jpowsour.2017.11.068_bib29 publication-title: IEEE Trans. components Packag. Technol. doi: 10.1109/TCAPT.2002.803653 – volume: 66 start-page: 2019 year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib21 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2016.2572163 – volume: 343 start-page: 216 year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib27 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2017.01.054 – volume: 226 start-page: 272 year: 2013 ident: 10.1016/j.jpowsour.2017.11.068_bib2 publication-title: J. power sources doi: 10.1016/j.jpowsour.2012.10.060 – volume: 115 start-page: 219 year: 2016 ident: 10.1016/j.jpowsour.2017.11.068_bib30 publication-title: Energy doi: 10.1016/j.energy.2016.08.109 – year: 2017 ident: 10.1016/j.jpowsour.2017.11.068_bib13 publication-title: Appl. Energy – volume: 149 start-page: 297 year: 2015 ident: 10.1016/j.jpowsour.2017.11.068_bib26 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.03.110 |
| SSID | ssj0001170 |
| Score | 2.551991 |
| Snippet | An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 191 |
| SubjectTerms | Battery pack model Battery pack state of health Particle filter Particle swarm optimization-genetic algorithm |
| Title | A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm |
| URI | https://dx.doi.org/10.1016/j.jpowsour.2017.11.068 |
| Volume | 376 |
| WOSCitedRecordID | wos000419810700024&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: 1873-2755 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001170 issn: 0378-7753 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLbKxgEOiJ9iDJAP3KKUpo5n51ihIUDThMRAhUsUO86arkuqklbb38I_y3uxnUZsaCDEpaqsOnX8vjx_fvH7HiGvgDNInUVxqBIJGxSV52HC5SiMjVGFNrlSbTmgL0fi-FhOp8nHweCHz4XZLERVyYuLZPlfTQ1tYGxMnf0Lc3cXhQb4DkaHTzA7fP6R4SdBVW_MolMLRzqoWhXNywA2yGdBm0PkSWIzC1BnwyYwBus2coAXdcISZV-xswb3cu7yNoNscVqvymZ2_ht6u8Tya4F9N9Dx9i48PV1vI_m25et6XnYgOyrX7iyAW1jbAwjWQ36rq9NZVvbDFZH0J5x9DO1KHo3N3YK9rBBWNnhorCuWgoVjYUV8va9mou9tI1voyy3cka20dGVNsOGJ-XAON453jef5xBClW21Bn1_0tj_hYHAskWARJt7eIrswjAS8_u7k_eH0Q7fQY9Ge9iWVG3wvAf36f7ue-_T4zMl9cs9Zik4sgB6Qgakekrs9ecpHZDmhLZSohxKtC-qgRBFKtIUStloo0S2UaAsluoUS7UOJ9qFEOyg9Jp_fHp68eRe6Ch2hjiPRhJGCR1ywjHNesJwnOo_HORBcpnQ2NpwlnMuCs1GsBON5YYwZZ7AjH-kIIw9Ssydkp6or85RQifWVka5rfRArmSkp8rjIkhH8-MCweI9wP3epdvL1WEVlkfpzivPUz3mKcw572xTmfI-87votrYDLjT0Sb5rU0VBLL1NA1A19n_1D331yZ_vAPCc7zWptXpDbetOU31cvHfh-ArICtq4 |
| 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=A+novel+approach+of+battery+pack+state+of+health+estimation+using+artificial+intelligence+optimization+algorithm&rft.jtitle=Journal+of+power+sources&rft.au=Zhang%2C+Xu&rft.au=Wang%2C+Yujie&rft.au=Liu%2C+Chang&rft.au=Chen%2C+Zonghai&rft.date=2018-02-01&rft.pub=Elsevier+B.V&rft.issn=0378-7753&rft.eissn=1873-2755&rft.volume=376&rft.spage=191&rft.epage=199&rft_id=info:doi/10.1016%2Fj.jpowsour.2017.11.068&rft.externalDocID=S0378775317315434 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0378-7753&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0378-7753&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0378-7753&client=summon |