Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression
State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many algorithms have been applied to perform prognostics for SOH estimation, especiall...
Saved in:
| Published in | Microelectronics and reliability Vol. 53; no. 6; pp. 832 - 839 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.06.2013
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many algorithms have been applied to perform prognostics for SOH estimation, especially data-driven prognostics algorithms supporting uncertainty representation and management. To describe the uncertainty in evaluation and prediction, we used the Gaussian Process Regression (GPR), a data-driven approach, to perform SOH prediction with mean and variance values as the uncertainty representation of SOH. Then, in order to realize multiple-step-ahead prognostics, we utilized an improved GPR method—combination Gaussian Process Functional Regression (GPFR)—to capture the actual trend of SOH, including global capacity degradation and local regeneration. Experimental results confirm that the proposed method can be effectively applied to lithium-ion battery monitoring and prognostics by quantitative comparison with the other GPR and GPFR models. |
|---|---|
| AbstractList | State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many algorithms have been applied to perform prognostics for SOH estimation, especially data-driven prognostics algorithms supporting uncertainty representation and management. To describe the uncertainty in evaluation and prediction, we used the Gaussian Process Regression (GPR), a data-driven approach, to perform SOH prediction with mean and variance values as the uncertainty representation of SOH. Then, in order to realize multiple-step-ahead prognostics, we utilized an improved GPR method—combination Gaussian Process Functional Regression (GPFR)—to capture the actual trend of SOH, including global capacity degradation and local regeneration. Experimental results confirm that the proposed method can be effectively applied to lithium-ion battery monitoring and prognostics by quantitative comparison with the other GPR and GPFR models. State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many algorithms have been applied to perform prognostics for SOH estimation, especially data-driven prognostics algorithms supporting uncertainty representation and management. To describe the uncertainty in evaluation and prediction, we used the Gaussian Process Regression (GPR), a data-driven approach, to perform SOH prediction with mean and variance values as the uncertainty representation of SOH. Then, in order to realize multiple-step-ahead prognostics, we utilized an improved GPR methodacombination Gaussian Process Functional Regression (GPFR)ato capture the actual trend of SOH, including global capacity degradation and local regeneration. Experimental results confirm that the proposed method can be effectively applied to lithium-ion battery monitoring and prognostics by quantitative comparison with the other GPR and GPFR models. |
| Author | Liu, Datong Pecht, Michael Peng, Yu Zhou, Jianbao Pang, Jingyue |
| Author_xml | – sequence: 1 givenname: Datong surname: Liu fullname: Liu, Datong email: liudatong@hit.edu.cn organization: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China – sequence: 2 givenname: Jingyue surname: Pang fullname: Pang, Jingyue organization: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China – sequence: 3 givenname: Jianbao surname: Zhou fullname: Zhou, Jianbao organization: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China – sequence: 4 givenname: Yu surname: Peng fullname: Peng, Yu organization: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China – sequence: 5 givenname: Michael surname: Pecht fullname: Pecht, Michael organization: CALCE, University of Maryland, College Park, MD 20742, USA |
| BookMark | eNqFUE1LAzEQzaGCbfUvyB69bM3HfoIHpWgVCnrQc0iys23KblKTrODJv27W1YsXYSCZN-89Zt4CzYw1gNAFwSuCSXF1WPVaOeugW1FM2ArHIniG5hjTIqUlyU7RwvsDxrjEhMzR57OzO2N90MonrXWJDyJAYttkD6IL-wTiqBdBWzOCnQ57PfTp2EoRAjgNPv48NEmElO2lNhN7IwbvtTDJ0VkFProPRo0T0SUOdi5CsTlDJ63oPJz_vEv0en_3sn5It0-bx_XtNlVZTkMqqoaKSrFcyKIEmYOiZStxWStSERA1I7QQRNYVJSVuW0YblolcqVrKtmpkzZbocvKN27wN8Sjea6-g64QBO3hOWEkLVrGsiNTriRqD9N5By5UO3zcFJ3THCeZj1vzAf7PmY9YcxyI4yos_8qOLCbqP_4U3kxBiDu8aHPdKg1HQaAcq8Mbq_yy-AL6zphU |
| CitedBy_id | crossref_primary_10_1016_j_actaastro_2018_10_029 crossref_primary_10_1214_24_AOAS1931 crossref_primary_10_1007_s40843_023_2665_8 crossref_primary_10_1016_j_microrel_2017_10_013 crossref_primary_10_1109_TII_2022_3168667 crossref_primary_10_1016_j_cirpj_2021_02_004 crossref_primary_10_1016_j_est_2022_104371 crossref_primary_10_3390_en6094682 crossref_primary_10_1016_j_jpowsour_2017_05_004 crossref_primary_10_1109_TR_2024_3359212 crossref_primary_10_1016_j_asoc_2019_02_035 crossref_primary_10_1016_j_jpowsour_2016_12_040 crossref_primary_10_3390_en15072498 crossref_primary_10_1016_j_jpowsour_2018_08_064 crossref_primary_10_1016_j_jpowsour_2022_231026 crossref_primary_10_1016_j_engfailanal_2016_04_014 crossref_primary_10_1080_00949655_2016_1202953 crossref_primary_10_1109_TR_2019_2957965 crossref_primary_10_1007_s00521_022_07291_5 crossref_primary_10_1016_j_est_2022_106319 crossref_primary_10_1016_j_ress_2022_108391 crossref_primary_10_1177_1748006X15579322 crossref_primary_10_3390_en12224366 crossref_primary_10_1109_TTE_2022_3226683 crossref_primary_10_1016_j_isci_2021_102060 crossref_primary_10_1016_j_jtte_2023_06_001 crossref_primary_10_1109_ACCESS_2020_2987426 crossref_primary_10_3390_app11041492 crossref_primary_10_1021_acs_iecr_3c02849 crossref_primary_10_1016_j_rineng_2023_101709 crossref_primary_10_3390_batteries11010026 crossref_primary_10_4028_www_scientific_net_MSF_929_93 crossref_primary_10_1007_s44196_023_00346_y crossref_primary_10_1016_j_slast_2024_100221 crossref_primary_10_1016_j_rser_2021_111903 crossref_primary_10_1109_ACCESS_2021_3110049 crossref_primary_10_1016_j_est_2021_103558 crossref_primary_10_3390_en14217206 crossref_primary_10_1149_2_0611807jes crossref_primary_10_1021_acs_chemrev_1c00108 crossref_primary_10_1080_08839514_2021_1901033 crossref_primary_10_1016_j_jclepro_2020_125700 crossref_primary_10_1109_TVT_2020_3000970 crossref_primary_10_1109_ACCESS_2019_2939593 crossref_primary_10_1115_1_4042987 crossref_primary_10_1109_TPEL_2024_3395180 crossref_primary_10_1108_IMDS_03_2019_0195 crossref_primary_10_1016_j_joule_2019_11_018 crossref_primary_10_1016_j_ifacol_2018_11_734 crossref_primary_10_3390_app8081301 crossref_primary_10_3390_en13040812 crossref_primary_10_1177_14759217221127252 crossref_primary_10_1016_j_jclepro_2020_120813 crossref_primary_10_1016_j_microrel_2017_06_045 crossref_primary_10_1088_1755_1315_93_1_012040 crossref_primary_10_1590_0001_3765202320201723 crossref_primary_10_3390_s16050623 crossref_primary_10_1109_TIM_2025_3541804 crossref_primary_10_3390_s22207835 crossref_primary_10_1016_j_microrel_2015_02_025 crossref_primary_10_3390_en14010218 crossref_primary_10_1038_s44172_024_00304_2 crossref_primary_10_1016_j_ijepes_2020_106369 crossref_primary_10_1016_j_egyr_2024_10_048 crossref_primary_10_1007_s13042_018_0865_y crossref_primary_10_1145_2883591_2883606 crossref_primary_10_3390_math10162991 crossref_primary_10_3390_batteries10060193 crossref_primary_10_1109_TR_2017_2742298 crossref_primary_10_1016_j_seta_2020_100752 crossref_primary_10_1109_TIA_2022_3170842 crossref_primary_10_1016_j_microrel_2015_06_133 crossref_primary_10_1016_j_ymssp_2023_110796 crossref_primary_10_1016_j_apenergy_2016_05_051 crossref_primary_10_1002_est2_542 crossref_primary_10_1016_j_jpowsour_2018_08_073 crossref_primary_10_3389_fenrg_2022_999676 crossref_primary_10_1016_j_ress_2019_03_015 crossref_primary_10_1016_j_ress_2020_107194 crossref_primary_10_3390_app131810243 crossref_primary_10_1007_s12541_023_00832_5 crossref_primary_10_1002_eem2_12386 crossref_primary_10_1016_j_ifacol_2017_08_863 crossref_primary_10_1016_j_est_2022_106193 crossref_primary_10_1016_j_est_2024_110575 crossref_primary_10_1109_TIE_2018_2842782 crossref_primary_10_1088_1742_6596_1132_1_012075 crossref_primary_10_1115_1_4049234 crossref_primary_10_1016_j_electacta_2022_141300 crossref_primary_10_1109_TIM_2016_2622838 crossref_primary_10_3390_en17071679 crossref_primary_10_4271_2015_01_9147 crossref_primary_10_1016_j_ress_2021_107542 crossref_primary_10_1016_j_isatra_2020_12_020 crossref_primary_10_1109_TIE_2018_2880703 crossref_primary_10_1016_j_seta_2022_102915 crossref_primary_10_1002_er_3598 crossref_primary_10_1007_s43236_021_00318_5 crossref_primary_10_1109_TIM_2017_2708204 crossref_primary_10_1002_er_5413 crossref_primary_10_1016_j_est_2023_107443 crossref_primary_10_1016_j_energy_2023_126706 crossref_primary_10_1016_j_est_2021_103644 crossref_primary_10_1109_TIM_2014_2348613 crossref_primary_10_1016_j_est_2021_103077 crossref_primary_10_1016_j_apenergy_2015_08_119 crossref_primary_10_1007_s43236_019_00016_3 crossref_primary_10_1039_D1TA00998B crossref_primary_10_1016_j_microrel_2018_03_032 crossref_primary_10_1016_j_ress_2018_02_022 crossref_primary_10_1002_eom2_12345 crossref_primary_10_1007_s00521_022_07322_1 crossref_primary_10_3390_en12122247 crossref_primary_10_1016_j_energy_2022_125503 crossref_primary_10_1016_j_jpowsour_2021_230024 crossref_primary_10_2514_1_I011235 crossref_primary_10_2514_1_I010385 crossref_primary_10_1109_TSMC_2015_2389757 crossref_primary_10_3390_wevj12030156 crossref_primary_10_1016_j_est_2021_102893 crossref_primary_10_1016_j_microrel_2021_114405 crossref_primary_10_1016_j_measurement_2014_11_031 crossref_primary_10_3390_electronics10222732 crossref_primary_10_1109_TR_2016_2570542 crossref_primary_10_1016_j_jclepro_2018_06_182 crossref_primary_10_1109_ACCESS_2022_3217478 crossref_primary_10_1109_TEC_2024_3385093 crossref_primary_10_1016_j_jpowsour_2023_233670 crossref_primary_10_1109_TMC_2017_2688321 crossref_primary_10_3390_en16155682 crossref_primary_10_1002_adts_202000109 crossref_primary_10_1016_j_rser_2023_114224 crossref_primary_10_3390_batteries10020060 crossref_primary_10_1016_j_est_2024_113623 crossref_primary_10_1557_mre_2018_11 crossref_primary_10_1016_j_ress_2022_108978 crossref_primary_10_1016_j_apenergy_2024_124629 crossref_primary_10_3390_en8042889 crossref_primary_10_2113_JEEG24_3_399 crossref_primary_10_1016_j_ijhydene_2022_12_135 crossref_primary_10_1109_TIA_2022_3210081 crossref_primary_10_1109_TIE_2016_2606588 crossref_primary_10_1016_j_jclepro_2021_125814 crossref_primary_10_17531_ein_2016_4_18 crossref_primary_10_1016_j_apenergy_2019_113813 crossref_primary_10_3390_en11123318 crossref_primary_10_1016_j_ymssp_2019_106486 crossref_primary_10_1016_j_rser_2021_111287 crossref_primary_10_1016_j_jpowsour_2015_01_164 crossref_primary_10_1155_2018_8358025 crossref_primary_10_1109_ACCESS_2021_3089032 crossref_primary_10_1016_j_energy_2022_125883 crossref_primary_10_1007_s00521_020_05105_0 crossref_primary_10_1177_1748006X14550662 crossref_primary_10_3390_machines10020072 crossref_primary_10_1115_1_4050798 crossref_primary_10_1016_j_est_2023_107698 crossref_primary_10_1109_ACCESS_2019_2905740 crossref_primary_10_1016_j_eswa_2018_08_033 crossref_primary_10_1016_j_cja_2017_11_004 crossref_primary_10_1109_TTE_2019_2944802 crossref_primary_10_1109_ACCESS_2018_2854224 crossref_primary_10_3390_batteries3040032 crossref_primary_10_3390_batteries8120290 crossref_primary_10_1016_j_est_2019_100951 crossref_primary_10_1109_TIE_2017_2764869 crossref_primary_10_1016_j_est_2023_106797 crossref_primary_10_1088_1742_6596_1123_1_012025 crossref_primary_10_1007_s00521_013_1520_x crossref_primary_10_1016_j_jpowsour_2019_227281 crossref_primary_10_1016_j_ymssp_2022_108917 crossref_primary_10_1142_S0218213018500367 crossref_primary_10_3233_JIFS_169549 crossref_primary_10_1016_j_est_2022_105992 crossref_primary_10_1088_1757_899X_793_1_012063 crossref_primary_10_1016_j_eswa_2021_116091 crossref_primary_10_1016_j_measurement_2019_07_064 crossref_primary_10_1016_j_est_2022_105073 crossref_primary_10_1155_2022_8264297 crossref_primary_10_1016_j_jpowsour_2020_228806 crossref_primary_10_1016_j_jpowsour_2021_230063 crossref_primary_10_1631_jzus_A2100006 crossref_primary_10_1038_s41598_024_63160_2 crossref_primary_10_1109_ACCESS_2020_3026552 crossref_primary_10_3390_app6060166 crossref_primary_10_1016_j_est_2023_108046 crossref_primary_10_1016_j_est_2023_109014 crossref_primary_10_1016_j_cja_2017_11_010 crossref_primary_10_3390_molecules27185762 crossref_primary_10_1016_j_apenergy_2022_119011 crossref_primary_10_1016_j_est_2021_103158 crossref_primary_10_3390_wevj12030113 crossref_primary_10_1016_j_est_2023_107513 crossref_primary_10_1016_j_xcrp_2021_100352 crossref_primary_10_1109_TIE_2018_2813964 crossref_primary_10_3390_wevj14090247 crossref_primary_10_1016_j_jpowsour_2016_09_120 crossref_primary_10_1016_j_rser_2019_109254 crossref_primary_10_1016_j_est_2024_110678 crossref_primary_10_1109_TETCI_2018_2880693 crossref_primary_10_1109_ACCESS_2019_2913163 crossref_primary_10_1016_j_conengprac_2024_105852 crossref_primary_10_1016_j_compstruct_2022_116579 crossref_primary_10_1016_j_rser_2020_110017 crossref_primary_10_1109_TII_2018_2794997 crossref_primary_10_1016_j_est_2023_109285 crossref_primary_10_1109_TIA_2021_3112392 crossref_primary_10_3390_en16052313 crossref_primary_10_1038_s41598_021_91241_z crossref_primary_10_3390_su14042313 crossref_primary_10_1155_2014_564894 crossref_primary_10_3390_app8060925 crossref_primary_10_1016_j_est_2022_104512 crossref_primary_10_1137_20M1315968 crossref_primary_10_3390_pr11030678 crossref_primary_10_1016_j_egyr_2021_09_131 crossref_primary_10_1016_j_ymssp_2017_11_016 crossref_primary_10_1177_01423312211040901 crossref_primary_10_1109_ACCESS_2017_2759094 crossref_primary_10_3390_en10111687 crossref_primary_10_1177_0142331216642836 crossref_primary_10_1016_j_ress_2014_06_014 crossref_primary_10_1109_TIM_2021_3111009 crossref_primary_10_1016_j_est_2021_102726 crossref_primary_10_3390_en14082117 crossref_primary_10_1016_j_est_2023_109037 crossref_primary_10_3390_pr7100661 crossref_primary_10_1080_23789689_2019_1708177 crossref_primary_10_1002_tcr_202200131 crossref_primary_10_3390_sym9010004 crossref_primary_10_1002_er_8536 crossref_primary_10_1016_j_est_2022_105978 crossref_primary_10_3390_en13020375 crossref_primary_10_3390_batteries9070351 crossref_primary_10_1002_qre_2688 crossref_primary_10_1007_s40747_023_01262_6 crossref_primary_10_3390_pr8050605 crossref_primary_10_1038_s41467_022_32422_w crossref_primary_10_1016_j_est_2021_103245 crossref_primary_10_1016_j_jclepro_2017_11_107 crossref_primary_10_3390_en9110896 crossref_primary_10_1039_D1EE01812D crossref_primary_10_3390_data3040049 crossref_primary_10_3390_en14092371 crossref_primary_10_1016_j_microrel_2017_03_021 crossref_primary_10_1007_s00521_020_05031_1 crossref_primary_10_1109_ACCESS_2022_3156657 crossref_primary_10_1109_TIE_2022_3167142 crossref_primary_10_1109_TNNLS_2021_3130812 crossref_primary_10_3390_en7106492 crossref_primary_10_1016_j_jpowsour_2020_228069 crossref_primary_10_1016_j_neucom_2020_07_081 crossref_primary_10_1007_s11668_022_01532_4 crossref_primary_10_1016_j_apenergy_2021_118348 crossref_primary_10_1016_j_est_2022_104984 crossref_primary_10_1016_j_mtener_2020_100537 crossref_primary_10_1016_j_electacta_2024_144360 crossref_primary_10_1016_j_jechem_2022_06_049 crossref_primary_10_1115_1_4028035 crossref_primary_10_1016_j_apenergy_2023_121660 crossref_primary_10_1149_2_1061814jes crossref_primary_10_1016_j_microrel_2017_03_007 crossref_primary_10_1016_j_microrel_2017_03_008 crossref_primary_10_1016_j_jpowsour_2016_04_119 crossref_primary_10_1109_ACCESS_2019_2923095 crossref_primary_10_1016_j_apenergy_2016_08_138 crossref_primary_10_1016_j_promfg_2019_06_156 crossref_primary_10_1016_j_energy_2022_124812 crossref_primary_10_1109_ACCESS_2019_2932719 crossref_primary_10_3390_batteries8040029 crossref_primary_10_1016_j_apenergy_2022_119624 crossref_primary_10_1038_s44296_024_00011_1 crossref_primary_10_1109_ACCESS_2019_2947294 crossref_primary_10_1016_j_apenergy_2018_03_053 crossref_primary_10_1016_j_jpowsour_2020_228716 crossref_primary_10_1007_s12239_021_0031_5 crossref_primary_10_1016_j_compchemeng_2023_108275 crossref_primary_10_1155_2019_5327319 crossref_primary_10_3390_wevj15020038 crossref_primary_10_1016_j_est_2020_101409 crossref_primary_10_1016_j_est_2021_102816 crossref_primary_10_3390_s18113916 crossref_primary_10_4271_2017_01_9078 crossref_primary_10_1016_j_est_2022_104720 crossref_primary_10_1016_j_rser_2023_114077 crossref_primary_10_1016_j_isatra_2020_03_007 crossref_primary_10_1016_j_prime_2024_100457 crossref_primary_10_1016_j_rser_2025_115408 crossref_primary_10_1177_0959651820950849 crossref_primary_10_3390_en11061420 crossref_primary_10_1016_j_apenergy_2018_10_095 crossref_primary_10_1109_TIM_2020_2996004 crossref_primary_10_1016_j_est_2023_107180 crossref_primary_10_1109_ACCESS_2020_3038182 crossref_primary_10_1007_s12598_022_02138_3 crossref_primary_10_1016_j_measurement_2017_08_030 crossref_primary_10_1109_TIA_2022_3170525 crossref_primary_10_3390_en17061272 crossref_primary_10_1016_j_jpowsour_2018_03_015 crossref_primary_10_1002_aic_14760 crossref_primary_10_1016_j_energy_2021_120851 crossref_primary_10_1016_j_est_2020_101410 crossref_primary_10_1109_TIM_2025_3548227 crossref_primary_10_3389_fenrg_2023_1059701 crossref_primary_10_1002_ente_202200402 crossref_primary_10_1016_j_ifacol_2020_11_064 |
| Cites_doi | 10.1109/AUTEST.2007.4374280 10.1109/MIM.2008.4579269 10.1109/PHM.2012.6228830 10.1111/j.1541-0420.2007.00758.x 10.1109/AERO.2008.4526631 10.1109/AERO.2003.1234169 10.1109/TIM.2012.2215142 10.1109/TIM.2008.2005965 10.1007/978-3-540-88682-2_36 10.1109/AUTEST.2008.4662684 10.1109/AERO.2010.5446841 10.1016/j.eswa.2011.03.063 10.1109/PHM.2012.6228849 10.1016/j.chemolab.2006.09.004 10.1016/j.jpowsour.2011.03.101 10.1109/TVT.2004.842461 10.24846/v19i3y201001 10.1109/TPEL.2009.2034966 10.1177/0142331208092030 10.1109/VPPC.2009.5289654 10.36001/phmconf.2011.v3i1.2073 10.1109/EUROCON.2011.5929179 |
| ContentType | Journal Article |
| Copyright | 2013 Elsevier Ltd |
| Copyright_xml | – notice: 2013 Elsevier Ltd |
| DBID | AAYXX CITATION 7SP 8FD L7M |
| DOI | 10.1016/j.microrel.2013.03.010 |
| DatabaseName | CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EndPage | 839 |
| ExternalDocumentID | 10_1016_j_microrel_2013_03_010 S0026271413000747 |
| GroupedDBID | --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 29M 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AABXZ AACTN AAEDT AAEDW AAEPC AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABWVN ABXDB ABXRA ACDAQ ACGFS ACNNM ACRLP ACRPL ACZNC ADBBV ADEZE ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEFWE AEIPS AEKER AENEX AEZYN AFJKZ AFRZQ AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD AXJTR AZFZN BJAXD BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM LY7 M41 MAGPM MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ RXW SDF SDG SES SET SEW SPC SPCBC SPD SSH SSM SST SSV SSZ T5K T9H TAE UHS UNMZH WUQ XOL ZMT ~G- AAYWO AAYXX ACVFH ADCNI AEUPX AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKYEP APXCP CITATION 7SP 8FD L7M |
| ID | FETCH-LOGICAL-c452t-a8d2a8c35ab67eb5ec27fb079c181ea93126a1b982170ff32d34a5cc9bbf8db93 |
| IEDL.DBID | .~1 |
| ISSN | 0026-2714 |
| IngestDate | Fri Jul 11 11:24:45 EDT 2025 Tue Jul 01 05:01:24 EDT 2025 Thu Apr 24 23:10:42 EDT 2025 Sun Apr 06 06:54:26 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c452t-a8d2a8c35ab67eb5ec27fb079c181ea93126a1b982170ff32d34a5cc9bbf8db93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1372638346 |
| PQPubID | 23500 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_1372638346 crossref_citationtrail_10_1016_j_microrel_2013_03_010 crossref_primary_10_1016_j_microrel_2013_03_010 elsevier_sciencedirect_doi_10_1016_j_microrel_2013_03_010 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2013-06-01 |
| PublicationDateYYYYMMDD | 2013-06-01 |
| PublicationDate_xml | – month: 06 year: 2013 text: 2013-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Microelectronics and reliability |
| PublicationYear | 2013 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Wei H, Nick W, Michael O, Michael P. Prognostics of Lithium-ion Batteries using Extended Kalman Filtering. In: IMAPS advanced technology workshop on high reliability microelectronics for military applications; 2011. p. 1–4. Williams CKI, Rasmussen CE. Gaussian processes for regression. In: Advances in neural information processing systems 8. MIT Press; 1996. Kozlowski JD. Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques. In: Proceedings of IEEE aerospace conference; 2003. p. 3257–70. Heng-Juan C, Qiang M, Wei L, Zhonglai W, Michael P. Application of unscented particle filter in remaining useful life prediction of lithium-ion batteries. In: 2012 Prognostics & system health management conference (PHM-2012 Beijing); 2012. p. 1–6. Shi, Wang (b0090) 2007; 63 Orchard, Tang, Saha, Goebel, Vachtsevanos (b0120) 2010; 19 Chaochao C, Michael P. Prognostics of lithium-ion batteries using model-based and data-driven methods. In: 2012 Prognostics & system health management conference. (PHM-2012 Beijing); 2012. p. 1–6. Ananth, Ming-Hsuan (b0150) 2008; 5302 Saha B, Goebel K. Battery data set, NASA ames prognostics data repository. NASA Ames, Moffett Field, CA; 2007. Jingliang, Jay (b0005) 2011; 196 Saha, Goebel, Poll, Christophersen (b0075) 2009; 58 Tao, Julian, Elaine (b0135) 2007; 87 Le D, Tang XD. Lithium-ion battery state of health estimation using ah-v characterization. In: Annual conference of the prognostics and health management society; 2011. p. 367–73. Nick W, Wei H, Michael P. Model based Battery Management System for Condition based Maintenance. In: MFPT 2012 proceedings: the prognostics and health management solutions conference; 2012. p. 1–13. Kejun Q, Chengke Z, Yue Y, Allan M. Temperature effect on electric vehicle battery cycle life in vehicle-to-grid applications. In: 2010 China international conference on electricity distribution (CICED); 2010. p. 1–6. Bhangu, Bentley, Stone, Bingham (b0015) 2011; 54 Jack, David, Aaron (b0155) 2005; 18 Bhaskar, Kai, Jon (b0025) 2009; 31 Kim (b0110) 2010; 25 Yunong Z, Leithead WE, Leith DJ. Time-series Gaussian process regression based on toeplitz computation of O(N2) operations and O(N)-level storage. In: Decision and control, 2005 and 2005 European control conference; 2005. p. 3711–16. Pattipati B, Pattipati K, Christopherson JP, Namburu SM, Prokhorov DV, Liu Q. Automotive battery management systems. In: 2008 IEEE Autotestcon; 2008. p. 581–6. Goebel, Saha, Saxena, Celaya, Christophersen (b0085) 2008; 4 Dai HF, Wei XZ, Sun ZC. A new SOH prediction concept for the power lithium-ion battery used on HEVs. In: Vehicle power and propulsion conference; 2009. p. 1649–53. Rasmussen, Williams (b0140) 2006 MacKay D. Introduction to Gaussian processes. Tech-nical report. UK: Cambridge University; 1997. <Seehttp://wol.ra.phy.cam.ac.uk/mackay/README.html>. . Alvin, Craig, Pritpal, Terrill, David (b0060) 1999; 80 Saha S, Saha B, Saxena A, Goebel K. Distributed prognostic health management with gaussian process regression, In: IEEE Aerospace conference; 2010. p. 1–8. Kocijan J, Tanko V. Prognosis of gear health using gaussian process model. In: 2011 IEEE international conference on computer as a tool (EUROCON); 2011. p. 1–4. Girard, Murray-Smith (b0170) 2005 Wei H, Williard N, Chaochao C, Michael P. State of charge estimation for electric vehicle batteries under an adaptive filtering framework. In: 2012 Prognostics & System Health Management Conference. (PHM-2012 Beijing); 2012. p. 1–5. Huimin C, Li XR. Distributed active learning with application to battery health management. In: Proceedings of the 14th international conference on information fusion (FUSION); 2011. p. 1–7. Anton S, Volker T. Transductive and Inductive methods for approximate gaussian process regression. In: Advances in nerual information processing systems 15. MIT Press; 2003. Widodo, Shim, Caesarendra, Yang (b0190) 2011; 38 Olivares, Cerda Munoz, Orchard, Silva (b0185) 2013; 62 Saha B, Goebel K. Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. In: Proceedings of the IEEE aerospace conference; 2008. p. 1–8. Saha B, Goebel K, Poll S, Christophersen J. An integrated approach to battery health monitoring using Bayesian regression and state estimation. In: 2007 IEEE Autotestcon; 2007. p. 646–53. Jingliang (10.1016/j.microrel.2013.03.010_b0005) 2011; 196 10.1016/j.microrel.2013.03.010_b0105 Alvin (10.1016/j.microrel.2013.03.010_b0060) 1999; 80 10.1016/j.microrel.2013.03.010_b0125 Olivares (10.1016/j.microrel.2013.03.010_b0185) 2013; 62 Kim (10.1016/j.microrel.2013.03.010_b0110) 2010; 25 Shi (10.1016/j.microrel.2013.03.010_b0090) 2007; 63 Bhangu (10.1016/j.microrel.2013.03.010_b0015) 2011; 54 Orchard (10.1016/j.microrel.2013.03.010_b0120) 2010; 19 Saha (10.1016/j.microrel.2013.03.010_b0075) 2009; 58 10.1016/j.microrel.2013.03.010_b0050 10.1016/j.microrel.2013.03.010_b0070 10.1016/j.microrel.2013.03.010_b0010 10.1016/j.microrel.2013.03.010_b0175 10.1016/j.microrel.2013.03.010_b0130 10.1016/j.microrel.2013.03.010_b0030 Ananth (10.1016/j.microrel.2013.03.010_b0150) 2008; 5302 10.1016/j.microrel.2013.03.010_b0035 10.1016/j.microrel.2013.03.010_b0055 Girard (10.1016/j.microrel.2013.03.010_b0170) 2005 Goebel (10.1016/j.microrel.2013.03.010_b0085) 2008; 4 Tao (10.1016/j.microrel.2013.03.010_b0135) 2007; 87 Jack (10.1016/j.microrel.2013.03.010_b0155) 2005; 18 10.1016/j.microrel.2013.03.010_b0160 10.1016/j.microrel.2013.03.010_b0180 10.1016/j.microrel.2013.03.010_b0080 10.1016/j.microrel.2013.03.010_b0065 10.1016/j.microrel.2013.03.010_b0020 10.1016/j.microrel.2013.03.010_b0040 Bhaskar (10.1016/j.microrel.2013.03.010_b0025) 2009; 31 10.1016/j.microrel.2013.03.010_b0145 10.1016/j.microrel.2013.03.010_b0045 10.1016/j.microrel.2013.03.010_b0100 Rasmussen (10.1016/j.microrel.2013.03.010_b0140) 2006 Widodo (10.1016/j.microrel.2013.03.010_b0190) 2011; 38 10.1016/j.microrel.2013.03.010_b0165 |
| References_xml | – reference: Heng-Juan C, Qiang M, Wei L, Zhonglai W, Michael P. Application of unscented particle filter in remaining useful life prediction of lithium-ion batteries. In: 2012 Prognostics & system health management conference (PHM-2012 Beijing); 2012. p. 1–6. – volume: 25 start-page: 1013 year: 2010 end-page: 1022 ident: b0110 article-title: A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer publication-title: IEEE Trans Power Electron – reference: Williams CKI, Rasmussen CE. Gaussian processes for regression. In: Advances in neural information processing systems 8. MIT Press; 1996. – volume: 31 start-page: 293 year: 2009 end-page: 308 ident: b0025 article-title: Comparison of prognostic algorithms for estimating remaining useful life of batteries publication-title: Trans Inst Measure Control – volume: 62 start-page: 364 year: 2013 end-page: 376 ident: b0185 article-title: Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena publication-title: IEEE T Instrum Meas – reference: Wei H, Williard N, Chaochao C, Michael P. State of charge estimation for electric vehicle batteries under an adaptive filtering framework. In: 2012 Prognostics & System Health Management Conference. (PHM-2012 Beijing); 2012. p. 1–5. – volume: 196 start-page: 6007 year: 2011 end-page: 6014 ident: b0005 article-title: A review on prognostics and health monitoring of Li-ion battery publication-title: J Power Sources – reference: Kocijan J, Tanko V. Prognosis of gear health using gaussian process model. In: 2011 IEEE international conference on computer as a tool (EUROCON); 2011. p. 1–4. – reference: Le D, Tang XD. Lithium-ion battery state of health estimation using ah-v characterization. In: Annual conference of the prognostics and health management society; 2011. p. 367–73. – reference: Huimin C, Li XR. Distributed active learning with application to battery health management. In: Proceedings of the 14th international conference on information fusion (FUSION); 2011. p. 1–7. – volume: 4 start-page: 33 year: 2008 end-page: 40 ident: b0085 article-title: Prognostics in battery health management publication-title: IEEE Instrum Meas Mag – reference: Pattipati B, Pattipati K, Christopherson JP, Namburu SM, Prokhorov DV, Liu Q. Automotive battery management systems. In: 2008 IEEE Autotestcon; 2008. p. 581–6. – volume: 58 start-page: 291 year: 2009 end-page: 296 ident: b0075 article-title: Prognostics methods for battery health monitoring using a Bayesian framework publication-title: Instrument Measure, IEEE Trans – reference: Kejun Q, Chengke Z, Yue Y, Allan M. Temperature effect on electric vehicle battery cycle life in vehicle-to-grid applications. In: 2010 China international conference on electricity distribution (CICED); 2010. p. 1–6. – volume: 5302 start-page: 468 year: 2008 end-page: 482 ident: b0150 article-title: Online sparse matrix gaussian process regression and vision applications publication-title: Lect Notes Comput Sci – reference: Saha B, Goebel K, Poll S, Christophersen J. An integrated approach to battery health monitoring using Bayesian regression and state estimation. In: 2007 IEEE Autotestcon; 2007. p. 646–53. – volume: 18 start-page: 1441 year: 2005 end-page: 1448 ident: b0155 article-title: Gaussian process dynamical models publication-title: Adv Neural Inform Process Syst (NIPS) – volume: 80 start-page: 293 year: 1999 end-page: 300 ident: b0060 article-title: Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology publication-title: J Power Sources – reference: Dai HF, Wei XZ, Sun ZC. A new SOH prediction concept for the power lithium-ion battery used on HEVs. In: Vehicle power and propulsion conference; 2009. p. 1649–53. – volume: 19 start-page: 209 year: 2010 end-page: 218 ident: b0120 article-title: Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices publication-title: Studies Inform Control – reference: Saha S, Saha B, Saxena A, Goebel K. Distributed prognostic health management with gaussian process regression, In: IEEE Aerospace conference; 2010. p. 1–8. – reference: >. – volume: 38 start-page: 11763 year: 2011 end-page: 11769 ident: b0190 article-title: Intelligent prognostics for battery health monitoring based on sample entropy publication-title: Expert Syst Appl – reference: Saha B, Goebel K. Battery data set, NASA ames prognostics data repository. NASA Ames, Moffett Field, CA; 2007. < – volume: 54 start-page: 783 year: 2011 end-page: 794 ident: b0015 article-title: Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles publication-title: IEEE Trans Vehicular Technol – reference: Chaochao C, Michael P. Prognostics of lithium-ion batteries using model-based and data-driven methods. In: 2012 Prognostics & system health management conference. (PHM-2012 Beijing); 2012. p. 1–6. – reference: Kozlowski JD. Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques. In: Proceedings of IEEE aerospace conference; 2003. p. 3257–70. – reference: Saha B, Goebel K. Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. In: Proceedings of the IEEE aerospace conference; 2008. p. 1–8. – volume: 63 start-page: 714 year: 2007 end-page: 723 ident: b0090 article-title: Gaussian process functional regression modeling for batch data publication-title: Biometrics – year: 2006 ident: b0140 article-title: Gaussian processes for machine learning – reference: MacKay D. Introduction to Gaussian processes. Tech-nical report. UK: Cambridge University; 1997. <Seehttp://wol.ra.phy.cam.ac.uk/mackay/README.html>. – reference: Nick W, Wei H, Michael P. Model based Battery Management System for Condition based Maintenance. In: MFPT 2012 proceedings: the prognostics and health management solutions conference; 2012. p. 1–13. – volume: 87 start-page: 59 year: 2007 end-page: 71 ident: b0135 article-title: Gaussian process regression for multivariate spectroscopic calibration publication-title: Chem Intell Lab Syst – reference: Wei H, Nick W, Michael O, Michael P. Prognostics of Lithium-ion Batteries using Extended Kalman Filtering. In: IMAPS advanced technology workshop on high reliability microelectronics for military applications; 2011. p. 1–4. – year: 2005 ident: b0170 article-title: Gaussian processes: prediction at a noisy input and application to iterative multiple-step ahead fore-casting of time-series. Switching and learning in feedback systems – reference: Yunong Z, Leithead WE, Leith DJ. Time-series Gaussian process regression based on toeplitz computation of O(N2) operations and O(N)-level storage. In: Decision and control, 2005 and 2005 European control conference; 2005. p. 3711–16. – reference: Anton S, Volker T. Transductive and Inductive methods for approximate gaussian process regression. In: Advances in nerual information processing systems 15. MIT Press; 2003. – year: 2006 ident: 10.1016/j.microrel.2013.03.010_b0140 – ident: 10.1016/j.microrel.2013.03.010_b0045 – ident: 10.1016/j.microrel.2013.03.010_b0070 doi: 10.1109/AUTEST.2007.4374280 – volume: 4 start-page: 33 year: 2008 ident: 10.1016/j.microrel.2013.03.010_b0085 article-title: Prognostics in battery health management publication-title: IEEE Instrum Meas Mag doi: 10.1109/MIM.2008.4579269 – ident: 10.1016/j.microrel.2013.03.010_b0035 doi: 10.1109/PHM.2012.6228830 – volume: 63 start-page: 714 year: 2007 ident: 10.1016/j.microrel.2013.03.010_b0090 article-title: Gaussian process functional regression modeling for batch data publication-title: Biometrics doi: 10.1111/j.1541-0420.2007.00758.x – ident: 10.1016/j.microrel.2013.03.010_b0175 doi: 10.1109/AERO.2008.4526631 – ident: 10.1016/j.microrel.2013.03.010_b0020 doi: 10.1109/AERO.2003.1234169 – volume: 62 start-page: 364 year: 2013 ident: 10.1016/j.microrel.2013.03.010_b0185 article-title: Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena publication-title: IEEE T Instrum Meas doi: 10.1109/TIM.2012.2215142 – ident: 10.1016/j.microrel.2013.03.010_b0010 – volume: 58 start-page: 291 year: 2009 ident: 10.1016/j.microrel.2013.03.010_b0075 article-title: Prognostics methods for battery health monitoring using a Bayesian framework publication-title: Instrument Measure, IEEE Trans doi: 10.1109/TIM.2008.2005965 – volume: 5302 start-page: 468 year: 2008 ident: 10.1016/j.microrel.2013.03.010_b0150 article-title: Online sparse matrix gaussian process regression and vision applications publication-title: Lect Notes Comput Sci doi: 10.1007/978-3-540-88682-2_36 – ident: 10.1016/j.microrel.2013.03.010_b0050 doi: 10.1109/AUTEST.2008.4662684 – ident: 10.1016/j.microrel.2013.03.010_b0165 doi: 10.1109/AERO.2010.5446841 – volume: 38 start-page: 11763 year: 2011 ident: 10.1016/j.microrel.2013.03.010_b0190 article-title: Intelligent prognostics for battery health monitoring based on sample entropy publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.03.063 – ident: 10.1016/j.microrel.2013.03.010_b0040 doi: 10.1109/PHM.2012.6228849 – volume: 87 start-page: 59 year: 2007 ident: 10.1016/j.microrel.2013.03.010_b0135 article-title: Gaussian process regression for multivariate spectroscopic calibration publication-title: Chem Intell Lab Syst doi: 10.1016/j.chemolab.2006.09.004 – year: 2005 ident: 10.1016/j.microrel.2013.03.010_b0170 – ident: 10.1016/j.microrel.2013.03.010_b0180 – volume: 196 start-page: 6007 issue: 15 year: 2011 ident: 10.1016/j.microrel.2013.03.010_b0005 article-title: A review on prognostics and health monitoring of Li-ion battery publication-title: J Power Sources doi: 10.1016/j.jpowsour.2011.03.101 – ident: 10.1016/j.microrel.2013.03.010_b0145 – volume: 54 start-page: 783 issue: 3 year: 2011 ident: 10.1016/j.microrel.2013.03.010_b0015 article-title: Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles publication-title: IEEE Trans Vehicular Technol doi: 10.1109/TVT.2004.842461 – volume: 19 start-page: 209 year: 2010 ident: 10.1016/j.microrel.2013.03.010_b0120 article-title: Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices publication-title: Studies Inform Control doi: 10.24846/v19i3y201001 – ident: 10.1016/j.microrel.2013.03.010_b0130 – volume: 80 start-page: 293 issue: 1–2 year: 1999 ident: 10.1016/j.microrel.2013.03.010_b0060 article-title: Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology publication-title: J Power Sources – ident: 10.1016/j.microrel.2013.03.010_b0055 – ident: 10.1016/j.microrel.2013.03.010_b0080 – volume: 25 start-page: 1013 year: 2010 ident: 10.1016/j.microrel.2013.03.010_b0110 article-title: A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer publication-title: IEEE Trans Power Electron doi: 10.1109/TPEL.2009.2034966 – ident: 10.1016/j.microrel.2013.03.010_b0160 – volume: 31 start-page: 293 year: 2009 ident: 10.1016/j.microrel.2013.03.010_b0025 article-title: Comparison of prognostic algorithms for estimating remaining useful life of batteries publication-title: Trans Inst Measure Control doi: 10.1177/0142331208092030 – ident: 10.1016/j.microrel.2013.03.010_b0105 doi: 10.1109/VPPC.2009.5289654 – ident: 10.1016/j.microrel.2013.03.010_b0030 – ident: 10.1016/j.microrel.2013.03.010_b0100 doi: 10.36001/phmconf.2011.v3i1.2073 – ident: 10.1016/j.microrel.2013.03.010_b0125 doi: 10.1109/EUROCON.2011.5929179 – volume: 18 start-page: 1441 year: 2005 ident: 10.1016/j.microrel.2013.03.010_b0155 article-title: Gaussian process dynamical models publication-title: Adv Neural Inform Process Syst (NIPS) – ident: 10.1016/j.microrel.2013.03.010_b0065 |
| SSID | ssj0007011 |
| Score | 2.536829 |
| Snippet | State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 832 |
| SubjectTerms | Algorithms Gaussian Health Lithium-ion batteries Monitoring Regression Representations Uncertainty |
| Title | Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression |
| URI | https://dx.doi.org/10.1016/j.microrel.2013.03.010 https://www.proquest.com/docview/1372638346 |
| Volume | 53 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8QwFA6iFz2IK-5E8BqnSdOkOQ7iOCoMHhS8lWzVEe0Ms1z1r5vXtG4IHoRemialvBfe0rzvewideCkdkzakJcbmhDMpSF5SR2zucumMsNrUBbID0b_jV_fZ_QI6a7EwUFbZ2P5o02tr3Yx0Gml2xsMhYHyZYJKCFa5p4AHBziXs8tPXzzIPmdDYNY8JArO_oISfTl-g6G3i4QiCpjXZKSBpf3dQP0x17X96a2i1CRxxN37bOlrw1QZa-UInuInebiYjqJsD5mUcglFco4XwqMQR7YiBUSNCFWEwBOCPw_kLgVtT02yGrBmDW3M4DAWJhKw5zr7Q8ymgLfE44gowuMP4FxFP_EOspa220F3v_PasT5oGC8TyjM2Izh3TuU0zbYT0JvOWydIkUtng971WKWVCU6PykLckZZkyl3KdWauMKXNnVLqNFqtR5XcQ5kYJ77LMKmE5D1ZUOhUiM2qpA5I0uYuyVqqFbdjHoQnGc9GWmT0VrTYK0EaRhIsmu6jzsW4c-Tf-XKFapRXfdlIRnMSfa49bLYfnUzg70ZUfzacFTSULpirlYu8f799Hyyy20yAJPUCLs8ncH4agZmaO6l17hJa6l9f9wTtzjftP |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jj9MwFLZKOcAc0LCJzrAYiavb2Ens-IgqOgVKxaGVerO8hWlF06rLdeavj1-cQEFIc0DKJY4dRX7OW-z3fQ-hD14Ix4QNYYmxBcmY4KQoqSO2cIVwhltt6gTZKR_Psy-LfNFBwxYLA2mVje6POr3W1k3LoJnNwXa5BIwv40xQ0MI1DfwD9DALvy-UMejf_M7zEAmNZfMYJ9D9BCa86q8h623n4QyCpjXbKUBp_22h_tLVtQEanaMnjeeIP8aPe4o6vnqGzk74BJ-j2--7DSTOAfUyDt4oruFCeFPiCHfEQKkRsYrQGDzw6-VxTeDW1DybIWzGYNccDk1hSkLYHHtf6eMe4JZ4G4EFGOxh3EbEO_8jJtNWL9B89Gk2HJOmwgKxWc4ORBeO6cKmuTZceJN7y0RpEiFtMPxey5QyrqmRRQhckrJMmUsznVsrjSkLZ2T6EnWrTeVfIZwZyb3Lcyu5zUAOwsngmlFLHbCkiR7K21lVtqEfhyoYP1WbZ7ZSrTQUSEMl4aJJDw1-jdtGAo57R8hWaOqPpaSClbh37PtWyuH5Hg5PdOU3x72iqWBBV6UZv_iP979Dj8azbxM1-Tz9eokes1hbgyT0Neoedkf_Jng4B_O2XsF3Jjr83Q |
| 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=Prognostics+for+state+of+health+estimation+of+lithium-ion+batteries+based+on+combination+Gaussian+process+functional+regression&rft.jtitle=Microelectronics+and+reliability&rft.au=Liu%2C+Datong&rft.au=Pang%2C+Jingyue&rft.au=Zhou%2C+Jianbao&rft.au=Peng%2C+Yu&rft.date=2013-06-01&rft.issn=0026-2714&rft.volume=53&rft.issue=6&rft.spage=832&rft.epage=839&rft_id=info:doi/10.1016%2Fj.microrel.2013.03.010&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0026-2714&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0026-2714&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0026-2714&client=summon |