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Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

Author

Listed:
  • Gaizka Saldaña

    (Department of Electrical Engineering, University of the Basque Country (UPV/EHU), Avda. Otaola 29, 20600 Eibar, Spain)

  • José Ignacio San Martín

    (Department of Electrical Engineering, University of the Basque Country (UPV/EHU), Avda. Otaola 29, 20600 Eibar, Spain)

  • Inmaculada Zamora

    (Department of Electrical Engineering, University of the Basque Country (UPV/EHU), Pza. Ingeniero Torres Quevedo s/n, 48013 Bilbao, Spain)

  • Francisco Javier Asensio

    (Department of Electrical Engineering, University of the Basque Country (UPV/EHU), Avda. Otaola 29, 20600 Eibar, Spain)

  • Oier Oñederra

    (Department of Electrical Engineering, University of the Basque Country (UPV/EHU), Pza. Ingeniero Torres Quevedo s/n, 48013 Bilbao, Spain)

Abstract

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.

Suggested Citation

  • Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2750-:d:249447
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    1. Haifeng Dai & Bo Jiang & Xuezhe Wei, 2018. "Impedance Characterization and Modeling of Lithium-Ion Batteries Considering the Internal Temperature Gradient," Energies, MDPI, vol. 11(1), pages 1-18, January.
    2. Eddahech, Akram & Briat, Olivier & Vinassa, Jean-Michel, 2015. "Performance comparison of four lithium–ion battery technologies under calendar aging," Energy, Elsevier, vol. 84(C), pages 542-550.
    3. Fotouhi, Abbas & Auger, Daniel J. & Propp, Karsten & Longo, Stefano & Wild, Mark, 2016. "A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1008-1021.
    4. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    5. Xiangwei Guo & Longyun Kang & Yuan Yao & Zhizhen Huang & Wenbiao Li, 2016. "Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm," Energies, MDPI, vol. 9(2), pages 1-16, February.
    6. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    7. Alexandros Nikolian & Yousef Firouz & Rahul Gopalakrishnan & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2016. "Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion," Energies, MDPI, vol. 9(5), pages 1-23, May.
    8. Joris De Hoog & Joris Jaguemont & Mohamed Abdel-Monem & Peter Van Den Bossche & Joeri Van Mierlo & Noshin Omar, 2018. "Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging," Energies, MDPI, vol. 11(4), pages 1-15, March.
    9. Daehyun Kim & Keunhwi Koo & Jae Jin Jeong & Taedong Goh & Sang Woo Kim, 2013. "Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model," Energies, MDPI, vol. 6(10), pages 1-14, October.
    10. Muhammad Umair Ali & Muhammad Ahmad Kamran & Pandiyan Sathish Kumar & Himanshu & Sarvar Hussain Nengroo & Muhammad Adil Khan & Altaf Hussain & Hee-Je Kim, 2018. "An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method," Energies, MDPI, vol. 11(11), pages 1-19, October.
    11. Tao Zhu & Haitao Min & Yuanbin Yu & Zhongmin Zhao & Tao Xu & Yang Chen & Xinyong Li & Cong Zhang, 2017. "An Optimized Energy Management Strategy for Preheating Vehicle-Mounted Li-ion Batteries at Subzero Temperatures," Energies, MDPI, vol. 10(2), pages 1-23, February.
    12. Shifei Yuan & Hongjie Wu & Xuerui Ma & Chengliang Yin, 2015. "Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration," Energies, MDPI, vol. 8(8), pages 1-23, July.
    13. Ximing Cheng & Liguang Yao & Yinjiao Xing & Michael Pecht, 2016. "Novel Parametric Circuit Modeling for Li-Ion Batteries," Energies, MDPI, vol. 9(7), pages 1-15, July.
    14. Ruifeng Zhang & Bizhong Xia & Baohua Li & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation," Energies, MDPI, vol. 11(9), pages 1-20, August.
    15. Kotub Uddin & Alessandro Picarelli & Christopher Lyness & Nigel Taylor & James Marco, 2014. "An Acausal Li-Ion Battery Pack Model for Automotive Applications," Energies, MDPI, vol. 7(9), pages 1-26, August.
    16. 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.
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