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...
Saved in:
Published in | World electric vehicle journal Vol. 11; no. 1; p. 23 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
MDPI AG
01.03.2020
|
Subjects | |
Online Access | Get full text |
ISSN | 2032-6653 2032-6653 |
DOI | 10.3390/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 |