Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which a...

Full description

Saved in:
Bibliographic Details
Published inWorld electric vehicle journal Vol. 11; no. 1; p. 23
Main Authors Zhang, Mingyue, Fan, Xiaobin
Format Journal Article
LanguageEnglish
Published MDPI AG 01.03.2020
Subjects
Online AccessGet full text
ISSN2032-6653
2032-6653
DOI10.3390/wevj11010023

Cover

Loading…
Abstract Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.
AbstractList Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.
Author Zhang, Mingyue
Fan, Xiaobin
Author_xml – sequence: 1
  givenname: Mingyue
  orcidid: 0000-0002-0259-0927
  surname: Zhang
  fullname: Zhang, Mingyue
– sequence: 2
  givenname: Xiaobin
  orcidid: 0000-0002-7187-8377
  surname: Fan
  fullname: Fan, Xiaobin
BookMark eNptkN1KAzEQhYNUsNbe-QB5AKv52aSbSy1VCxbBv9tlNjvppqyNZENL395tK1LEqxnmnPk4nHPSW4UVEnLJ2bWUht1scL3knHHGhDwhfcGkGGmtZO9oPyPDtl2yzsIzwznvk_kLrj1uaFjRVCN9TZCQBkcnNcQF0mmb_Cck38lzTHWoWupCpNMGbYre0g-svW2Q3kFKGLcX5NRB0-LwZw7I-_30bfI4enp-mE1un0a2i5pGUKkKHGAl1Jgr4UqpeSaMKEsQ1kGOvNQyh5JDZbXJS-OAa2klVM5ox5UckNmBWwVYFl-xyxi3RQBf7A8hLgqIaZesQCudMKByY8eZyCSYrGJjZXKmpZJGdKyrA8vG0LYR3S-Ps2LXbHHcbGcXf-zWp31DKYJv_n_6Bob6fg4
CitedBy_id crossref_primary_10_1080_15325008_2023_2202672
crossref_primary_10_1109_ACCESS_2023_3294430
crossref_primary_10_1109_TDSC_2023_3299522
crossref_primary_10_1155_2023_3648488
crossref_primary_10_3390_wevj15060229
crossref_primary_10_1016_j_measurement_2023_114026
crossref_primary_10_1007_s12667_023_00631_x
crossref_primary_10_3390_electronics10131588
crossref_primary_10_3390_wevj11030050
crossref_primary_10_1002_er_7713
crossref_primary_10_1016_j_jclepro_2022_134279
crossref_primary_10_1109_ACCESS_2023_3327728
crossref_primary_10_3390_jlpea14040059
crossref_primary_10_1109_ACCESS_2024_3418909
crossref_primary_10_1016_j_est_2023_106904
crossref_primary_10_1016_j_measurement_2024_115148
crossref_primary_10_3390_sym17030321
crossref_primary_10_3390_en17071643
crossref_primary_10_1007_s00202_024_02917_4
crossref_primary_10_1016_j_est_2024_114943
crossref_primary_10_1016_j_est_2021_103518
crossref_primary_10_1109_TVT_2023_3237173
crossref_primary_10_3390_wevj13070124
crossref_primary_10_1002_cta_3397
crossref_primary_10_3390_wevj11030049
crossref_primary_10_1016_j_mset_2023_05_003
crossref_primary_10_1016_j_est_2023_107629
crossref_primary_10_3390_vehicles4040071
crossref_primary_10_3390_en18020238
crossref_primary_10_47836_pjst_32_2_20
crossref_primary_10_1016_j_geits_2024_100175
crossref_primary_10_1016_j_est_2024_111089
crossref_primary_10_1149_1945_7111_ad5efa
crossref_primary_10_3390_electronics13163316
crossref_primary_10_1016_j_etran_2024_100326
crossref_primary_10_1177_01445987231211943
crossref_primary_10_3390_s22010357
crossref_primary_10_7467_KSAE_2022_30_1_061
crossref_primary_10_1016_j_est_2021_103309
crossref_primary_10_3390_app14156648
crossref_primary_10_3390_en13143658
crossref_primary_10_1016_j_egyr_2024_04_041
crossref_primary_10_1007_s00202_023_02227_1
crossref_primary_10_32604_cmc_2022_030490
crossref_primary_10_3390_wevj15020072
crossref_primary_10_3390_batteries8090119
crossref_primary_10_1093_ijlct_ctae127
crossref_primary_10_1109_ACCESS_2023_3267164
crossref_primary_10_1016_j_prime_2024_100856
crossref_primary_10_36548_jeea_2024_2_005
crossref_primary_10_1016_j_rineng_2024_103843
crossref_primary_10_1016_j_energy_2023_128461
crossref_primary_10_1016_j_seta_2022_102801
crossref_primary_10_1109_ACCESS_2022_3146410
crossref_primary_10_1016_j_est_2023_107573
crossref_primary_10_1109_LCSYS_2022_3230010
crossref_primary_10_3389_fenrg_2024_1453711
Cites_doi 10.1109/ICMSE.2014.6930374
10.1109/TIE.2013.2259779
10.1109/EEEIC.2016.7555760
10.1109/SPIT.2018.8350462
10.1016/j.apenergy.2014.12.021
10.1109/APEC.2011.5744869
10.1109/TIE.2014.2341576
10.1109/TTE.2015.2512237
10.1016/j.jpowsour.2015.08.036
10.1016/j.apenergy.2017.08.124
10.1109/IWISA.2009.5073210
10.1016/j.rser.2017.05.001
10.1109/ISKE.2017.8258840
10.1002/9781118900239
10.1063/1.5064479
10.1016/j.apenergy.2017.05.183
10.1016/0002-9149(65)90056-1
10.1016/j.jpowsour.2004.02.033
10.1016/j.jpowsour.2014.02.026
10.1109/ACCESS.2017.2725301
10.1109/ICMA.2014.6886014
10.1016/j.jpowsour.2015.03.157
10.1016/j.jpowsour.2017.01.098
10.1109/IECON.2017.8216914
10.1016/j.jpowsour.2012.10.060
10.1109/ACCESS.2018.2837156
10.1109/TTE.2015.2437338
10.1109/CAC.2017.8243786
10.1109/TIE.2015.2403796
10.1109/ICCPCT.2013.6528901
10.1109/IECON.2015.7392264
10.1109/TII.2013.2284713
10.1109/SEGE.2018.8499462
10.1016/j.apenergy.2008.11.021
10.1016/j.energy.2016.08.109
10.1016/j.jpowsour.2014.09.146
10.1109/MPE.2017.2708812
10.1109/TIE.2017.2733475
10.1016/j.measurement.2017.11.016
10.1016/j.energy.2018.04.085
10.1049/iet-est.2013.0020
10.1016/j.jpowsour.2016.03.112
ContentType Journal Article
DBID AAYXX
CITATION
DOA
DOI 10.3390/wevj11010023
DatabaseName CrossRef
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
DatabaseTitleList
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 2032-6653
ExternalDocumentID oai_doaj_org_article_ec3f29a589c74243a94d075980635392
10_3390_wevj11010023
GroupedDBID AADQD
AAFWJ
AAYXX
ADBBV
AFPKN
AFZYC
ALMA_UNASSIGNED_HOLDINGS
BCNDV
CITATION
GROUPED_DOAJ
HCIFZ
MODMG
M~E
OK1
ID FETCH-LOGICAL-c339t-ad5dafaed257152fb3614292bba2cfa8e1b638ab1adc698b9fa163c3adf96f153
IEDL.DBID DOA
ISSN 2032-6653
IngestDate Wed Aug 27 01:19:34 EDT 2025
Thu Apr 24 23:06:33 EDT 2025
Tue Jul 01 00:22:50 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License https://creativecommons.org/licenses/by/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c339t-ad5dafaed257152fb3614292bba2cfa8e1b638ab1adc698b9fa163c3adf96f153
ORCID 0000-0002-7187-8377
0000-0002-0259-0927
OpenAccessLink https://doaj.org/article/ec3f29a589c74243a94d075980635392
ParticipantIDs doaj_primary_oai_doaj_org_article_ec3f29a589c74243a94d075980635392
crossref_primary_10_3390_wevj11010023
crossref_citationtrail_10_3390_wevj11010023
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2020-03-01
PublicationDateYYYYMMDD 2020-03-01
PublicationDate_xml – month: 03
  year: 2020
  text: 2020-03-01
  day: 01
PublicationDecade 2020
PublicationTitle World electric vehicle journal
PublicationYear 2020
Publisher MDPI AG
Publisher_xml – name: MDPI AG
References ref_50
ref_14
ref_58
ref_13
Pei (ref_28) 2013; 3
ref_57
ref_12
Lin (ref_29) 2017; 205
ref_56
ref_55
Bilgin (ref_1) 2015; 1
ref_52
ref_51
Weiqin (ref_81) 2018; 26
ref_18
ref_17
ref_16
Hannan (ref_59) 2017; 78
Xiong (ref_5) 2017; 65
Sun (ref_15) 2016; 162
Zhang (ref_61) 2016; 115
Lu (ref_11) 2013; 226
Xia (ref_54) 2018; 153
ref_60
Yang (ref_43) 2018; 6
Jianshu (ref_72) 2017; 36
Wu (ref_80) 2016; 46
Shichang (ref_71) 2019; 32
Liu (ref_67) 2016; 320
ref_69
ref_24
ref_23
ref_22
ref_66
ref_21
ref_65
ref_20
ref_64
ref_63
ref_62
Farmann (ref_32) 2017; 347
Ng (ref_36) 2009; 86
Gholizadeh (ref_26) 2013; 61
Xu (ref_39) 2018; 18
Lipu (ref_53) 2018; 6
ref_70
Klass (ref_85) 2015; 298
Hu (ref_2) 2017; 15
Tong (ref_74) 2015; 293
Zhang (ref_3) 2015; 62
Zhang (ref_78) 2013; 38
ref_35
ref_79
ref_34
Peng (ref_9) 2017; 5
ref_33
ref_77
ref_76
Chaoui (ref_10) 2014; 62
ref_31
ref_75
ref_30
ref_38
Jun (ref_73) 2015; 15
ref_37
Hu (ref_4) 2015; 2
Zou (ref_68) 2015; 273
ref_83
Weng (ref_27) 2014; 258
XinTian (ref_88) 2019; 34
Hu (ref_8) 2013; 10
ref_46
Wu (ref_87) 2019; 11
ref_45
ref_42
ref_86
ref_41
Chen (ref_44) 2018; 116
ref_84
Peishan (ref_47) 2019; 15
Lillehei (ref_19) 1965; 16
Yang (ref_6) 2017; 207
ref_49
ref_48
Plett (ref_25) 2004; 134
Qian (ref_82) 2018; 41
Tingting (ref_40) 2018; 6
ref_7
References_xml – ident: ref_55
  doi: 10.1109/ICMSE.2014.6930374
– ident: ref_49
– volume: 61
  start-page: 1335
  year: 2013
  ident: ref_26
  article-title: Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model
  publication-title: IEEE Trans. Ind. Electron.
  doi: 10.1109/TIE.2013.2259779
– ident: ref_7
  doi: 10.1109/EEEIC.2016.7555760
– ident: ref_84
– ident: ref_16
– ident: ref_63
  doi: 10.1109/SPIT.2018.8350462
– ident: ref_65
– volume: 41
  start-page: 7
  year: 2018
  ident: ref_82
  article-title: Accurate estimation of charge state of lithium battery base on fuzzy control
  publication-title: Electron. Meas. Technol.
– volume: 38
  start-page: 61
  year: 2013
  ident: ref_78
  article-title: Comparison and application of multiple regression and BP neural network prediction model
  publication-title: J. Kunming Univ. Sci. Technol.
– volume: 162
  start-page: 1399
  year: 2016
  ident: ref_15
  article-title: A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2014.12.021
– ident: ref_41
  doi: 10.1109/APEC.2011.5744869
– ident: ref_42
– volume: 62
  start-page: 1610
  year: 2014
  ident: ref_10
  article-title: Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries
  publication-title: IEEE Trans. Ind. Electron.
  doi: 10.1109/TIE.2014.2341576
– ident: ref_35
– ident: ref_23
– ident: ref_58
– volume: 2
  start-page: 140
  year: 2015
  ident: ref_4
  article-title: Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles
  publication-title: IEEE Trans. Transp. Electrif.
  doi: 10.1109/TTE.2015.2512237
– volume: 298
  start-page: 92
  year: 2015
  ident: ref_85
  article-title: Capturing lithium-ion battery dynamics with support vector machine-based battery model
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2015.08.036
– volume: 205
  start-page: 892
  year: 2017
  ident: ref_29
  article-title: A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2017.08.124
– ident: ref_77
– ident: ref_57
  doi: 10.1109/IWISA.2009.5073210
– volume: 78
  start-page: 834
  year: 2017
  ident: ref_59
  article-title: A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
  publication-title: Renew. Sustain. Energy Rev.
  doi: 10.1016/j.rser.2017.05.001
– ident: ref_62
  doi: 10.1109/ISKE.2017.8258840
– ident: ref_20
  doi: 10.1002/9781118900239
– volume: 11
  start-page: 1
  year: 2019
  ident: ref_87
  article-title: SOC estimation for batteries using MS-AUKF and neural network
  publication-title: J. Renew. Sustain. Energy
  doi: 10.1063/1.5064479
– ident: ref_31
– volume: 207
  start-page: 336
  year: 2017
  ident: ref_6
  article-title: A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2017.05.183
– ident: ref_52
– volume: 36
  start-page: 1428
  year: 2017
  ident: ref_72
  article-title: An Improved Particle Filter Algorithm for SOC Estimation of Electric Vehicle Battery
  publication-title: Mech. Sci. Technol.
– volume: 16
  start-page: 717
  year: 1965
  ident: ref_19
  article-title: A new method of assessing the state of charge of implanted cardiac pacemaker batteries
  publication-title: Am. J. Cardiol.
  doi: 10.1016/0002-9149(65)90056-1
– ident: ref_48
– ident: ref_69
– ident: ref_83
– volume: 134
  start-page: 277
  year: 2004
  ident: ref_25
  article-title: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2004.02.033
– volume: 258
  start-page: 228
  year: 2014
  ident: ref_27
  article-title: A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2014.02.026
– volume: 5
  start-page: 13202
  year: 2017
  ident: ref_9
  article-title: State of charge estimation of battery energy storage systems based on adaptive unscented Kalman filter with a noise statistics estimator
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2017.2725301
– ident: ref_13
– ident: ref_38
– ident: ref_17
– ident: ref_45
– volume: 34
  start-page: 535
  year: 2019
  ident: ref_88
  article-title: CKF estimation Li-ion battery SOC based on Drift-Ah integral method
  publication-title: Control Decis.
– ident: ref_56
  doi: 10.1109/ICMA.2014.6886014
– volume: 293
  start-page: 416
  year: 2015
  ident: ref_74
  article-title: On-line optimization of battery open circuit voltage for improved state-of-charge and state-of-health estimation
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2015.03.157
– volume: 347
  start-page: 1
  year: 2017
  ident: ref_32
  article-title: A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2017.01.098
– ident: ref_66
  doi: 10.1109/IECON.2017.8216914
– volume: 226
  start-page: 272
  year: 2013
  ident: ref_11
  article-title: A review on the key issues for lithium-ion battery management in electric vehicles
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2012.10.060
– ident: ref_30
– ident: ref_76
– ident: ref_24
– ident: ref_34
– volume: 6
  start-page: 28150
  year: 2018
  ident: ref_53
  article-title: State of charge estimation for lithium-ion battery using recurrent NARX neural network model based lighting search algorithm
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2837156
– volume: 15
  start-page: 103
  year: 2015
  ident: ref_73
  article-title: Estimation for SOC of PEV Battery Based on Artificial Immune Particle Filter
  publication-title: Transp. Syst. Eng. Inf.
– ident: ref_86
– volume: 1
  start-page: 4
  year: 2015
  ident: ref_1
  article-title: Making the case for electrified transportation
  publication-title: IEEE Trans. Transp. Electrif.
  doi: 10.1109/TTE.2015.2437338
– ident: ref_64
  doi: 10.1109/CAC.2017.8243786
– volume: 62
  start-page: 4948
  year: 2015
  ident: ref_3
  article-title: Robust and adaptive estimation of state of charge for lithium-ion batteries
  publication-title: IEEE Trans. Ind. Electron.
  doi: 10.1109/TIE.2015.2403796
– ident: ref_51
  doi: 10.1109/ICCPCT.2013.6528901
– volume: 46
  start-page: 16
  year: 2016
  ident: ref_80
  article-title: Estimating SOC of Li-ion battery by improved AH combined with neural network
  publication-title: Battery Bimon.
– ident: ref_14
  doi: 10.1109/IECON.2015.7392264
– ident: ref_37
– volume: 10
  start-page: 1948
  year: 2013
  ident: ref_8
  article-title: Model-based dynamic power assessment of lithium-ion batteries considering different operating conditions
  publication-title: IEEE Trans. Ind. Inform.
  doi: 10.1109/TII.2013.2284713
– ident: ref_18
– ident: ref_12
  doi: 10.1109/SEGE.2018.8499462
– volume: 86
  start-page: 1506
  year: 2009
  ident: ref_36
  article-title: Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2008.11.021
– ident: ref_21
– volume: 115
  start-page: 219
  year: 2016
  ident: ref_61
  article-title: An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model
  publication-title: Energy
  doi: 10.1016/j.energy.2016.08.109
– volume: 273
  start-page: 793
  year: 2015
  ident: ref_68
  article-title: Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2014.09.146
– ident: ref_79
– volume: 15
  start-page: 20
  year: 2017
  ident: ref_2
  article-title: Technological developments in batteries: A survey of principal roles, types, and management needs
  publication-title: IEEE Power Energy Mag.
  doi: 10.1109/MPE.2017.2708812
– volume: 65
  start-page: 1526
  year: 2017
  ident: ref_5
  article-title: A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries
  publication-title: IEEE Trans. Ind. Electron.
  doi: 10.1109/TIE.2017.2733475
– volume: 6
  start-page: 58
  year: 2018
  ident: ref_40
  article-title: SOC Estimation and Simulation of Lithium Battery Based on Improved Ampere-hour Integral Method
  publication-title: Energy Sav. New Energy
– volume: 116
  start-page: 586
  year: 2018
  ident: ref_44
  article-title: A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity
  publication-title: Measurement
  doi: 10.1016/j.measurement.2017.11.016
– volume: 153
  start-page: 694
  year: 2018
  ident: ref_54
  article-title: State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network
  publication-title: Energy
  doi: 10.1016/j.energy.2018.04.085
– ident: ref_75
– volume: 18
  start-page: 9
  year: 2018
  ident: ref_39
  article-title: Lithium-Ion Battery SOC Estimation Based on Ahh-total Integration Method
  publication-title: Appl. Technol.
– ident: ref_50
– ident: ref_33
– volume: 32
  start-page: 104
  year: 2019
  ident: ref_71
  article-title: Research on SOC Estimation Algorithm of Lithium Battery Based on Iterative Kalman Particle Filter
  publication-title: Ind. Control Comput.
– volume: 3
  start-page: 112
  year: 2013
  ident: ref_28
  article-title: Relaxation model of the open-circuit voltage for state-of-charge estimation in lithium-ion batteries
  publication-title: Iet Electr. Syst. Transp.
  doi: 10.1049/iet-est.2013.0020
– ident: ref_46
– volume: 26
  start-page: 145
  year: 2018
  ident: ref_81
  article-title: Lithium Battery SOC Prediction Based on Multiple Linear Regression Model
  publication-title: Comput. Meas. Control
– volume: 6
  start-page: 183
  year: 2018
  ident: ref_43
  article-title: SOC estimation of lithium-ion battery based on improved ampere-hour integral method
  publication-title: Chin. J. Power Sources
– ident: ref_70
– ident: ref_60
– ident: ref_22
– volume: 320
  start-page: 1
  year: 2016
  ident: ref_67
  article-title: A new method of modeling and state of charge estimation of the battery
  publication-title: J. Power Sources
  doi: 10.1016/j.jpowsour.2016.03.112
– volume: 15
  start-page: 15
  year: 2019
  ident: ref_47
  article-title: Analysis of SOC Estimation Algorithm for Electric Vehicle Power Battery
  publication-title: Automot. Pract. Technol.
SSID ssj0002149111
Score 2.4277654
SecondaryResourceType review_article
Snippet Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely...
SourceID doaj
crossref
SourceType Open Website
Enrichment Source
Index Database
StartPage 23
SubjectTerms batteries
control-theory-driven estimation
electric vehicles
multi-scale
state of charge
test-driven estimation
Title Review on the State of Charge Estimation Methods for Electric Vehicle Battery
URI https://doaj.org/article/ec3f29a589c74243a94d075980635392
Volume 11
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA6yJz2IT1xf5KAnCWvTpDZHlV0WYT25sreSpw-kFV0V_70zSV3qQbx46aFMQ5jp5PumTb4h5EiWwWZGBmY8V0xId8pKbRwrnFTiDB7iHg84T66L8VRczeSs0-oL94QleeDkuIG3eeBKy1JZqOJErpVwAHOqBGyVAO64-gLmdYopXIM5EH_I4rTTPYe6fvDh3x8B6lBxNP-BQR2p_ogpozWy2pJBep4msU6WfL1BVjoSgZtkkj7e06amQNVo5Ia0CRR_k995OoQMTYcP6ST2gn6lwELpMDa3ebD01t_j0DTJaH5ukeloeHM5Zm0LBGZh1nOmnXQ6aO8gswBpg8kBTrnixmhugy59ZiCBtMm0s4UqjQoaCJbNtQuqCLCabZNe3dR-h1DBXWEk9hhTwDkyZ4QttOdwtUAzjOuTk2-nVLbVB8c2FU8V1Anowqrrwj45Xlg_J12MX-wu0L8LG1SzjjcgxlUb4-qvGO_-xyB7ZJljrRz3j-2T3vzlzR8AoZibw_jufAH9jsex
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=Review+on+the+State+of+Charge+Estimation+Methods+for+Electric+Vehicle+Battery&rft.jtitle=World+electric+vehicle+journal&rft.au=Zhang%2C+Mingyue&rft.au=Fan%2C+Xiaobin&rft.date=2020-03-01&rft.issn=2032-6653&rft.eissn=2032-6653&rft.volume=11&rft.issue=1&rft.spage=23&rft_id=info:doi/10.3390%2Fwevj11010023&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_wevj11010023
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2032-6653&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2032-6653&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2032-6653&client=summon