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Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

Author

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  • Theodoros Kalogiannis

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Md Sazzad Hosen

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Mohsen Akbarzadeh Sokkeh

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Shovon Goutam

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Joris Jaguemont

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Lu Jin

    (Global Energy Interconnection Research Institute Europe GmbH, 10623 Berlin, Germany)

  • Geng Qiao

    (Global Energy Interconnection Research Institute Europe GmbH, 10623 Berlin, Germany)

  • Maitane Berecibar

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Joeri Van Mierlo

    (ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

Abstract

A lithium-ion battery cell’s electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light- and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs’ parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

Suggested Citation

  • Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4031-:d:279542
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    1. Zhu, Jiangong & Knapp, Michael & Darma, Mariyam Susana Dewi & Fang, Qiaohua & Wang, Xueyuan & Dai, Haifeng & Wei, Xuezhe & Ehrenberg, Helmut, 2019. "An improved electro-thermal battery model complemented by current dependent parameters for vehicular low temperature application," Applied Energy, Elsevier, vol. 248(C), pages 149-161.
    2. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    3. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    4. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    5. Zubi, Ghassan & Dufo-López, Rodolfo & Carvalho, Monica & Pasaoglu, Guzay, 2018. "The lithium-ion battery: State of the art and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 292-308.
    6. Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
    7. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    8. Mousavi G., S.M. & Nikdel, M., 2014. "Various battery models for various simulation studies and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 477-485.
    9. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    10. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    11. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
    12. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    13. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
    14. Noshin Omar & Mohamed Daowd & Peter van den Bossche & Omar Hegazy & Jelle Smekens & Thierry Coosemans & Joeri van Mierlo, 2012. "Rechargeable Energy Storage Systems for Plug-in Hybrid Electric Vehicles—Assessment of Electrical Characteristics," Energies, MDPI, vol. 5(8), pages 1-37, August.
    15. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    16. Cunxue Wu & Rujian Fu & Zhongming Xu & Yang Chen, 2017. "Improved State of Charge Estimation for High Power Lithium Ion Batteries Considering Current Dependence of Internal Resistance," Energies, MDPI, vol. 10(10), pages 1-17, September.
    17. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    18. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    19. Dong, Guangzhong & Wei, Jingwen & Zhang, Chenbin & Chen, Zonghai, 2016. "Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method," Applied Energy, Elsevier, vol. 162(C), pages 163-171.
    20. Thomas R. B. Grandjean & Andrew McGordon & Paul A. Jennings, 2017. "Structural Identifiability of Equivalent Circuit Models for Li-Ion Batteries," Energies, MDPI, vol. 10(1), pages 1-16, January.
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