Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

1D electrochemical model of lithium-ion battery for a sizing methodology of thermal power plant integrated storage system

1 General Electric GEEPF, Rue de la découverte, 90000 Belfort, France
2 GREEN, 2 avenue de la forêt de Haye, 54500 Vandœuvre-lès-Nancy, France
3 LEMTA, 2 avenue de la forêt de Haye, 54500 Vandœuvre-lès-Nancy, France

Special Issues: Intelligent Battery Power System Design and Simulation

The interest of using storage system has been investigated by grid operators over the past decades. Currently, some power plant manufacturers propose to integrate the storage system into thermal plants to meet grid codes requirements and improve the plant operability. Thermal powerplants change the way of the generation becoming peaking or cycling unit instead of baseload unit as few decades ago. This change of grid operability compels the power plant operators to enhance their flexibility, emission and efficiency. Storage systems seem to be a promising solution that can help achieve this change. Regarding the cost, it is mainly driven by the battery elements (a third of the total cost). Therefore, an oversizing of the solution will decrease the economic benefits. This article proposes a new electrochemical model of Li-ion battery implemented in a sizing methodology to enhance the business case. This method will also include asset losses, battery aging model and plant profiles. The new electrical model is based on the single particle and single electrode. It mainly focuses on the energy aspect and it exhibits good accuracy for the study purpose. Moreover, it has a simplified configuration using only supplier datasheet which logically leads to loss of accuracy. The model assessment is achieved by several battery technologies (Lithium Titanate Oxide, Lithium Iron Phosphate and Nickel Manganese Cobalt) and shows its use restrictions. At last, different plant profiles are run to show the benefits of the proposed approach.
  Article Metrics

Keywords Lithium-ion battery; Single particle model; Battery energy storage system; Battery sizing methodology

Citation: François KREMER, Stéphane RAEL, Matthieu URBAIN. 1D electrochemical model of lithium-ion battery for a sizing methodology of thermal power plant integrated storage system. AIMS Energy, 2020, 8(5): 721-748. doi: 10.3934/energy.2020.5.721


  • 1. GE Power, General Electric PI-BESS Leaflet., 2016. Available from: https://www.ge.com/content/dam/gepower-pgdp/global/en_US/documents/product/power%20plants/pi-bess-leaflet-20160624.pdf.
  • 2. Energy-Storage news, Articles on the hybridization of a gas turbine and a BESS, 2016. Available from: https://www.energy-storage.news/news/ge-southern-california-edison-introduce-first-battery-storage-gas-turbine-h.
  • 3. Delille G, Francois B, Malarange G (2012) Dynamic frequency control support by energy storage to reduce the impact of wind and solar generation on isolated power system's inertia. IEEE Trans Sustainable Energy 3: 931-939.    
  • 4. Kremer F, Remy D, Merville W, et al. (2020) Battery energy storage system integration in a combined Cycle power plant for the purpose of the angular and voltage stability. In: Németh B, Ekonomou L (Eds.), Flexitranstore, Cham, Springer International Publishing, 84-94.
  • 5. Kremer F, Buquet M, Biellmann H, et al. (2019) Analysis of battery energy storage system integration in a combined cycle power plant. 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, IEEE, 1-6.
  • 6. Technical information on the Enhanced frequency response services. National Grid ESO. Available from: https://www.nationalgrideso.com/balancing-services/frequency-response-services/frequency-auction-trial.
  • 7. Yang Y, Bremner S, Menictas C, et al. (2018) Battery energy storage system size determination in renewable energy systems: A review. Renewable Sustainable Energy Rev 91: 109-125.    
  • 8. San Martín I, Berrueta A, Sanchis P, et al. (2018) Methodology for sizing stand-alone hybrid systems: A case study of a traffic control system. Energy 153: 870-881.    
  • 9. Samba A (2015) Battery Electrical Vehicles-Analysis of Thermal Modelling and Thermal Management. Electrical power. Université de caen Basse Normandie, Vrije Universiteit Brussel, 2015. English.
  • 10. Astaneh M, Roshandel R, Dufo-López R, et al. (2018) A novel framework for optimization of size and control strategy of lithium-ion battery based off-grid renewable energy systems. Energy Convers Manage 175: 99-111.    
  • 11. Bouabdallah A, Olivier JC, Bourguet S, et al. (2015) Safe sizing methodology applied to a standalone photovoltaic system. Renewable Energy 80: 266-274.    
  • 12. Meng J, Luo G, Ricco M, et al. (2018) Overview of lithium-ion battery modeling methods for state-of-charge estimation in electrical vehicles. Appl Sci 8: 1-17.
  • 13. Wang Y, Liu C, Pan R, et al. (2017) Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator. Energy 121: 739-750.    
  • 14. Urbain M, Raël S, Davat B, et al. (2007) State estimation of a lithium ion battery through kalman filter. 2007 Power Electronics Specialists Conference, Orlando, USA, IEEE, 2804-2810.
  • 15. Lin X, Pereza HE, Mohan S, et al. (2014) A lumped-parameter electro-thermal model for cylindrical batteries. J Power Sources 257: 1-11.    
  • 16. Wang Y, Gao G, Li X, et al. (2020) A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory. J Power Sources 449: 1-12.
  • 17. Urbain M, Hinaje M, Raël S, et al. (2010) Energetical modeling of lithium-ion batteries including electrode porosity effects. IEEE Trans Energy Convers 25: 862-872.    
  • 18. Smith K, Wang CY (2006) Solid-state diffusion limitations on pulse operation of a lithium ion cell for hybrid electric vehicles. J Power Sources 161: 628-639.    
  • 19. Legrand N, Knosp B, Desprez P, et al. (2014) Physical characterization of the charging process of a Li-ion battery and prediction of Li plating by electrochemical modelling. J Power Sources 245: 208-216.    
  • 20. Doyle M, Fuller TF, Newman J (1993) Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J Electrochem Soc 140: 1526-1533.
  • 21. Fuller TF, Doyle M, Newman J (1994) Simulation and optimization of the dual lithium ion insertion cell. J Electrochem Soc 141: 1-10.
  • 22. Smith K, Wang CY (2006) Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles. J Power Sources 160: 662-673.    
  • 23. Kai L, White LE (2011) Mathematical modeling of a lithium ion battery with thermal effects in COMSOL Inc. Multiphysics (MP) software. J Power Sources 196: 5985-5989.    
  • 24. Di Domenico D, Stefanopoulou A, Fiengo G (2010) Lithium-Ion battery state of charge and critical surface charge estimation using an electrochemical model-based extended kalman filter. J Dyn Syst, Meas, Control 132: 061302.    
  • 25. Dey S, Ayalew B, Pisu P (2015) Nonlinear robust observers for State-of-Charge estimation of Lithium-Ion cells based on a reduced electrochemical Model. IEEE Trans Control Syst Technol 23: 1935-1942.    
  • 26. Blondel P, Postoyan P, Raël S, et al. (2019) Nonlinear circle-criterion observer design for an electrochemical battery model. IEEE Trans Control Syst Technol 27: 889-897.    
  • 27. Moura SJ, Bribiesca Argomedo F, Klein R, et al. (2017) Battery state estimation for a single particle model with electrolyte dynamics. IEEE Trans Control Syst Technol 25: 453-468.    
  • 28. Forman JC, Bashash S, Stein JL, et al. (2011) Reduction of an electrochemistry-based Li-ion battery model via quasi-linearization and Padé approximation. J Electrochem Soc 158: A93-A101.    
  • 29. Smith K, Rahn C, Wang CY (2007) Control oriented 1D electrochemical model of lithium ion battery. Energy Convers Manage 48: 2565-2578.    
  • 30. Meyers J, Doyle M, Darling R, et al. (2000) The impedance response of a porous electrode composed of intercalation particles. J Electrochem Soc 147: 2930-2940.    
  • 31. Summerfield JH, Curtis CN (2015) Modeling the Lithium Ion/Electrode Battery Interface Using Fick's Second Law of Diffusion, the Laplace Transform, Charge Transfer Functions, and a [4, 4] Padé Approximant. Int J Electrochem 2015: 496905.
  • 32. Tran NT, Vilathgamuwa M, Farrell T, et al. (2018) A padé approximate model of lithium ion batteries. J Electrochem Soc 165: A1409-A1421.    
  • 33. Sarasketa-Zabala E, Laresgoiti I, Alava I, et al. (2013) Validation of the methodology for lithium-ion batteries lifetime prognosis. 2013 World Electric Vehicle Symposium and Exhibition (EVS27), Barcelona, Spain, IEEE, 1-12.


Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved