Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach
•State equations are derived from the battery equivalent circuit model.•Improved unscented Kalman filter approach composed of model adaptive and noise adaptive was introduced.•Experiment of sensitivity analysis was designed.•The experimental results revealed the effectiveness of the purposed approac...
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| Published in | Applied Mathematical Modelling Vol. 70; pp. 532 - 544 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Inc
01.06.2019
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0307-904X 1088-8691 0307-904X |
| DOI | 10.1016/j.apm.2019.01.031 |
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| Abstract | •State equations are derived from the battery equivalent circuit model.•Improved unscented Kalman filter approach composed of model adaptive and noise adaptive was introduced.•Experiment of sensitivity analysis was designed.•The experimental results revealed the effectiveness of the purposed approach.
An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach’s estimation error is less than 1.79% with acceptable robustness and time complexity. |
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| AbstractList | •State equations are derived from the battery equivalent circuit model.•Improved unscented Kalman filter approach composed of model adaptive and noise adaptive was introduced.•Experiment of sensitivity analysis was designed.•The experimental results revealed the effectiveness of the purposed approach.
An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach’s estimation error is less than 1.79% with acceptable robustness and time complexity. An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach’s estimation error is less than 1.79% with acceptable robustness and time complexity. |
| Author | Wang, Youren He, Zhijia Yang, Liwen Chen, Zewang Zhao, Xiaobing |
| Author_xml | – sequence: 1 givenname: Zewang surname: Chen fullname: Chen, Zewang email: czwnuaa306@163.com – sequence: 2 givenname: Liwen surname: Yang fullname: Yang, Liwen – sequence: 3 givenname: Xiaobing surname: Zhao fullname: Zhao, Xiaobing – sequence: 4 givenname: Youren surname: Wang fullname: Wang, Youren – sequence: 5 givenname: Zhijia surname: He fullname: He, Zhijia |
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| Cites_doi | 10.1016/j.jpowsour.2014.02.026 10.3390/en11051211 10.1109/TIE.2013.2259779 10.1016/j.energy.2011.03.059 10.5194/ars-14-55-2016 10.1109/TSTE.2016.2574755 10.1016/j.electacta.2016.06.042 10.1016/S0378-7753(01)00560-2 10.1016/j.jprocont.2012.01.002 10.1016/j.apm.2016.01.047 10.11591/telkomnika.v11i8.2629 10.1016/j.apenergy.2013.07.008 10.1016/j.jpowsour.2014.10.036 10.1016/j.jpowsour.2014.07.016 10.1016/j.jpowsour.2014.07.103 10.1109/TIE.2010.2043035 10.1149/07212.0013ecst |
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| References | Weng, Sun, Peng (bib0004) 2014; 258 Sun, Hu, Yuan, Li (bib0018) 2014; 36 Huangfu, Xu, Zhao, Liu, Gao (bib0001) 2018; 11 He, Dong, Pan, Long, Wang (bib0012) 2016; 211 Huang, Chen, Guibas (bib0019) 2014 Hu, Hu, Lin, Li, Jiang, Qiu, Li (bib0009) 2014; 269 Charkhgard, Farrokhi (bib0017) 2010; 57 Gholizadeh, Salmasi (bib0014) 2014; 61 Piller, Perrin, Jossen (bib0002) 2001; 96 Jansen, Vollnhals, Renner, Vergossen, John, Götze (bib0005) 2016; 14 Burgos, Saez, Orchard, Cardenas (bib0008) 2015; 274 Zhu, Sun, Rong, Liu (bib0010) 2013; 11 Mirzaee, Salahshoor (bib0021) 2012; 22 Paschero, Storti, Rizzi, Mascioli, Rizzoni (bib0011) 2016; 7 Zhang, Wei, Qi (bib0016) 2016; 40 Stroe, Knap, Swierczynski, Stanciu, Schaltz, Teodorescu (bib0006) 2016; 72 Guo, Hu, Li (bib0003) 2015 Hunt, Motloch (bib0020) 2003 Xing, He, Pecht, Tsui (bib0007) 2014; 113 Xia, Chen, Tian, Sun, Xu, Zheng (bib0015) 2014; 270 Du, Wang, Wen (bib0013) 2013 Huang (10.1016/j.apm.2019.01.031_bib0019) 2014 Gholizadeh (10.1016/j.apm.2019.01.031_bib0014) 2014; 61 Huangfu (10.1016/j.apm.2019.01.031_bib0001) 2018; 11 He (10.1016/j.apm.2019.01.031_bib0012) 2016; 211 Piller (10.1016/j.apm.2019.01.031_bib0002) 2001; 96 Du (10.1016/j.apm.2019.01.031_bib0013) 2013 Jansen (10.1016/j.apm.2019.01.031_bib0005) 2016; 14 Stroe (10.1016/j.apm.2019.01.031_bib0006) 2016; 72 Zhang (10.1016/j.apm.2019.01.031_bib0016) 2016; 40 Xing (10.1016/j.apm.2019.01.031_bib0007) 2014; 113 Weng (10.1016/j.apm.2019.01.031_bib0004) 2014; 258 Hu (10.1016/j.apm.2019.01.031_bib0009) 2014; 269 Xia (10.1016/j.apm.2019.01.031_bib0015) 2014; 270 Mirzaee (10.1016/j.apm.2019.01.031_bib0021) 2012; 22 Burgos (10.1016/j.apm.2019.01.031_bib0008) 2015; 274 Paschero (10.1016/j.apm.2019.01.031_bib0011) 2016; 7 Hunt (10.1016/j.apm.2019.01.031_bib0020) 2003 Zhu (10.1016/j.apm.2019.01.031_bib0010) 2013; 11 Guo (10.1016/j.apm.2019.01.031_bib0003) 2015 Charkhgard (10.1016/j.apm.2019.01.031_bib0017) 2010; 57 Sun (10.1016/j.apm.2019.01.031_bib0018) 2014; 36 |
| References_xml | – start-page: 1458 year: 2015 end-page: 1460 ident: bib0003 article-title: The SOC estimation of battery based on the method of improved ampere-hour and Kalman filter publication-title: Industrial Electronics and Applications – volume: 57 start-page: 4178 year: 2010 end-page: 4187 ident: bib0017 article-title: State-of-charge estimation for lithium-ion batteries using neural networks and EKF publication-title: IEEE Trans. Ind. Electron. – volume: 72 start-page: 13 year: 2016 end-page: 22 ident: bib0006 article-title: An electrochemical impedance spectroscopy study on a lithium sulfur pouch cell publication-title: ECS Trans. – volume: 270 start-page: 359 year: 2014 end-page: 366 ident: bib0015 article-title: A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer publication-title: J. Power Sources – start-page: 64 year: 2014 end-page: 72 ident: bib0019 article-title: Scalable semidefinite relaxation for maximum a posterior estimation publication-title: International Conference on Machine Learning – volume: 258 start-page: 228 year: 2014 end-page: 237 ident: bib0004 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 – volume: 96 start-page: 113 year: 2001 end-page: 120 ident: bib0002 article-title: Methods for state-of-charge determination and their applications publication-title: J. Power Sources – volume: 36 start-page: 3531 year: 2014 end-page: 3540 ident: bib0018 article-title: Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles publication-title: Energy – volume: 211 start-page: 101 year: 2016 end-page: 109 ident: bib0012 article-title: State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF publication-title: Electrochim. Acta – volume: 22 start-page: 626 year: 2012 end-page: 634 ident: bib0021 article-title: Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller publication-title: J. Process Control – volume: 274 start-page: 355 year: 2015 end-page: 366 ident: bib0008 article-title: Fuzzy modelling for the state-of-charge estimation of lead-acid batteries publication-title: J. Power Sources – volume: 11 start-page: 4208 year: 2013 end-page: 4213 ident: bib0010 article-title: SOC EKF estimation based on a second-order LiFePO4 battery model publication-title: Telkomnika – year: 2003 ident: bib0020 publication-title: Freedom Car Battery Test Manual for Power-Assist Hybrid Electric Vehicles – volume: 14 start-page: 55 year: 2016 end-page: 62 ident: bib0005 article-title: Advanced binary search pattern for impedance spectra classification for determining the state of charge of a lithium iron phosphate cell using a support vector machine publication-title: Adv. Radio Sci. – volume: 269 start-page: 682 year: 2014 end-page: 693 ident: bib0009 article-title: State-of-charge estimation for battery management system using optimized support vector machine for regression publication-title: J. Power Sources – volume: 11 start-page: 1211 year: 2018 ident: bib0001 article-title: A novel battery state of charge estimation method based on a super-twisting sliding mode observer publication-title: Energies – volume: 113 start-page: 106 year: 2014 end-page: 115 ident: bib0007 article-title: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures publication-title: Appl. Energy – volume: 7 start-page: 1695 year: 2016 end-page: 1702 ident: bib0011 article-title: A novel mechanical analogy-based battery model for SOC estimation using a multicell EKF publication-title: IEEE Trans. Sustainable Energy – start-page: 580 year: 2013 end-page: 585 ident: bib0013 article-title: Li-ion battery SOC estimation using particle filter based on an equivalent circuit model publication-title: 2013 10th IEEE International Conference on Control and Automation (ICCA) – volume: 61 start-page: 1335 year: 2014 end-page: 1344 ident: bib0014 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. – volume: 40 start-page: 6040 year: 2016 end-page: 6050 ident: bib0016 article-title: State of charge estimation of LiFePO4 batteries based on online parameter identification publication-title: Appl. Math. Modell. – start-page: 64 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0019 article-title: Scalable semidefinite relaxation for maximum a posterior estimation – volume: 258 start-page: 228 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0004 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: 11 start-page: 1211 issue: 5 year: 2018 ident: 10.1016/j.apm.2019.01.031_bib0001 article-title: A novel battery state of charge estimation method based on a super-twisting sliding mode observer publication-title: Energies doi: 10.3390/en11051211 – start-page: 580 year: 2013 ident: 10.1016/j.apm.2019.01.031_bib0013 article-title: Li-ion battery SOC estimation using particle filter based on an equivalent circuit model – volume: 61 start-page: 1335 issue: 3 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0014 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 – volume: 36 start-page: 3531 issue: 5 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0018 article-title: Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles publication-title: Energy doi: 10.1016/j.energy.2011.03.059 – volume: 14 start-page: 55 issue: C year: 2016 ident: 10.1016/j.apm.2019.01.031_bib0005 article-title: Advanced binary search pattern for impedance spectra classification for determining the state of charge of a lithium iron phosphate cell using a support vector machine publication-title: Adv. Radio Sci. doi: 10.5194/ars-14-55-2016 – volume: 7 start-page: 1695 issue: 4 year: 2016 ident: 10.1016/j.apm.2019.01.031_bib0011 article-title: A novel mechanical analogy-based battery model for SOC estimation using a multicell EKF publication-title: IEEE Trans. Sustainable Energy doi: 10.1109/TSTE.2016.2574755 – volume: 211 start-page: 101 year: 2016 ident: 10.1016/j.apm.2019.01.031_bib0012 article-title: State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2016.06.042 – volume: 96 start-page: 113 issue: 1 year: 2001 ident: 10.1016/j.apm.2019.01.031_bib0002 article-title: Methods for state-of-charge determination and their applications publication-title: J. Power Sources doi: 10.1016/S0378-7753(01)00560-2 – volume: 22 start-page: 626 issue: 3 year: 2012 ident: 10.1016/j.apm.2019.01.031_bib0021 article-title: Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller publication-title: J. Process Control doi: 10.1016/j.jprocont.2012.01.002 – volume: 40 start-page: 6040 issue: 11–12 year: 2016 ident: 10.1016/j.apm.2019.01.031_bib0016 article-title: State of charge estimation of LiFePO4 batteries based on online parameter identification publication-title: Appl. Math. Modell. doi: 10.1016/j.apm.2016.01.047 – volume: 11 start-page: 4208 issue: 8 year: 2013 ident: 10.1016/j.apm.2019.01.031_bib0010 article-title: SOC EKF estimation based on a second-order LiFePO4 battery model publication-title: Telkomnika doi: 10.11591/telkomnika.v11i8.2629 – start-page: 1458 year: 2015 ident: 10.1016/j.apm.2019.01.031_bib0003 article-title: The SOC estimation of battery based on the method of improved ampere-hour and Kalman filter – volume: 113 start-page: 106 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0007 article-title: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures publication-title: Appl. Energy doi: 10.1016/j.apenergy.2013.07.008 – volume: 274 start-page: 355 year: 2015 ident: 10.1016/j.apm.2019.01.031_bib0008 article-title: Fuzzy modelling for the state-of-charge estimation of lead-acid batteries publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2014.10.036 – volume: 269 start-page: 682 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0009 article-title: State-of-charge estimation for battery management system using optimized support vector machine for regression publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2014.07.016 – year: 2003 ident: 10.1016/j.apm.2019.01.031_bib0020 – volume: 270 start-page: 359 year: 2014 ident: 10.1016/j.apm.2019.01.031_bib0015 article-title: A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2014.07.103 – volume: 57 start-page: 4178 issue: 12 year: 2010 ident: 10.1016/j.apm.2019.01.031_bib0017 article-title: State-of-charge estimation for lithium-ion batteries using neural networks and EKF publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2010.2043035 – volume: 72 start-page: 13 issue: 12 year: 2016 ident: 10.1016/j.apm.2019.01.031_bib0006 article-title: An electrochemical impedance spectroscopy study on a lithium sulfur pouch cell publication-title: ECS Trans. doi: 10.1149/07212.0013ecst |
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| Snippet | •State equations are derived from the battery equivalent circuit model.•Improved unscented Kalman filter approach composed of model adaptive and noise adaptive... An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to... |
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| SubjectTerms | Adaptive algorithms Adaptive filters Algorithms Kalman filters Li-ion battery Lithium-ion batteries Mathematical models Model adaptive Noise adaptive Parameter identification Parameter sensitivity Robustness Sensitivity analysis State of charge State of charge estimation Unscented Kalman filter |
| Title | Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach |
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