Skip to main content

Advertisement

Log in

Lifetime estimation of grid connected LiFePO4 battery energy storage systems

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Battery Energy Storage Systems (BESS) are becoming strong alternatives to improve the flexibility, reliability and security of the electric grid, especially in the presence of Variable Renewable Energy Sources. Hence, it is essential to investigate the performance and life cycle estimation of batteries which are used in the stationary BESS for primary grid applications. In this paper, a new approach is proposed to investigate life cycle and performance of Lithium iron Phosphate (LiFePO4) batteries for real-time grid applications. The proposed accelerated lifetime model is based on real-time operational parameters of the battery such as temperature, State of Charge, Depth of Discharge and Open Circuit Voltage. Also, performance analysis of LiFePO4 battery system has been carried out for different grid-scale applications. Proposed methodology helps to design the size of the battery system for particular grid applications. Applicability and reliability of the developed life cycle estimation model are demonstrated on the practical 500 kW/250kWh LiFePO4 battery system installed at 230/110/22 kV grid connected substation at Puducherry, India. The real-time operational challenges are addressed and recommendations made based on the field data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Poullikkas A (2013) A comparative overview of large-scale battery systems for electricity storage. Renew Sustain Energy Rev 27:778–788. https://doi.org/10.1016/j.rser.2013.07.017

    Article  Google Scholar 

  2. Vazquez S, Lukic SM, Galvan E, Franquelo LG, Carrasco JM (2010) Energy storage systems for transport and grid applications. IEEETrans Ind Electron 57(12):3881–3895. https://doi.org/10.1109/TIE.2010.2076414

    Article  Google Scholar 

  3. Hesse HC, Schimpe M, Kucevic D (2017) Lithium-ion battery storage for the grid—a review of stationary battery storage system design tailored for applications in modern power grids. Energies 10:2107. https://doi.org/10.3390/en10122107

    Article  Google Scholar 

  4. Stroe D-I, Knap V, Swierczynski M, Stroe A-I, Teodorescu R (2016) Operation of grid-connected lithium-ion battery energy storage system for primary frequency regulation: a battery lifetime perspective. IEEE Trans Ind Appl. https://doi.org/10.1109/TIA.2016.2616319

    Article  Google Scholar 

  5. Stroe D-I, Swierczynski M, Stroe A-I (2016) Degradation behaviour of lithium-ion batteries based on field measured frequency regulation mission profile. In: Proceedings of the 2015 IEEE energy conversion congress and exposition (ECCE), Doi: https://doi.org/10.1109/ECCE.2015.7309663

  6. Thien T, Schweer D, Stein DV, Moser A, Sauer DU (2017) Real-world operating strategy and sensitivity analysis of frequency containment reserve provision with battery energy storage systems in the german market. J Energy Storage 13:143–163. https://doi.org/10.1016/j.est.2017.06.012

    Article  Google Scholar 

  7. Arfeen ZA, Abdullah MP, Hassan R, Othman BM, Siddique A (2020) Energy storage usages: Engineering reactions, economic-technological values for electric vehicles—A technological outlook. Int Trans Electr Energy Syst. https://doi.org/10.1002/2050-7038.12422

    Article  Google Scholar 

  8. Hannana MA, Lipu MSH, Hussain A, Mohamed A (2017) A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renew Sustain Energy Rev 78:834–854. https://doi.org/10.1016/j.rser.2017.05.001

    Article  Google Scholar 

  9. Yang J, Dong H, Huang Y, Cai L, Gou F, He Z (2018) Coordinated optimization of vehicle-to-grid control and load frequency control by considering statistical properties of active power imbalance. Int Trans Electr Energy Syst. https://doi.org/10.1002/etep.2750

    Article  Google Scholar 

  10. De Gennaroa M, Paffumia E, Martinia G, Giallonardo A (2019) A case study to predict the capacity fade of the battery of electrified vehicles in real-world use conditions. Case Stud Transp Policy. https://doi.org/10.1016/j.cstp.2019.11.005

    Article  Google Scholar 

  11. Subburajn AS, Pushpakaran BN, Bayne SB (2015) Overview of grid connected renewable energy based battery projects in USA. Renew Sustain Energy Rev 45:219–234. https://doi.org/10.1016/j.rser.2015.01.052

    Article  Google Scholar 

  12. Arteaga J, Zareipour H, Thangadurai V (2017) Overview of lithium-ion grid-scale energy storage systems. Curr Sustain Renew Energy Rep. https://doi.org/10.1007/s40518-017-0086-0

    Article  Google Scholar 

  13. Wang D, Ma N, Gao Y, Hu Y, Zhang C (2018) Participation in primary frequency regulation of wind turbines using hybrid control method. Int Trans Electr Energy Syst. https://doi.org/10.1002/etep.2527

    Article  Google Scholar 

  14. Das CK, Mahmoud TS, Bass O (2020) Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response. J Energy storage 29:101315. https://doi.org/10.1016/j.est.2020.101315

    Article  Google Scholar 

  15. Stroe A-I, Knap V, Stroe D-I (2018) Comparison of lithium-ion battery performance at beginning-of-life and end of-life. Microelectron Reliab. https://doi.org/10.1016/j.microrel.2018.07.077

    Article  Google Scholar 

  16. Datta U, Kalam A, Shi J (2019) The relevance of large-scale battery energy storage (BES) application in providing primary frequency control with increased wind energy penetration. J Energy Storage 23:9–18. https://doi.org/10.1016/j.est.2019.02.013

    Article  Google Scholar 

  17. Nejada S, Gladwina DT, Stone DA (2016) Systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. J Power Sources 316:183–196. https://doi.org/10.1016/j.jpowsour.2016.03.042

    Article  Google Scholar 

  18. Swierczynski M, Stroe D-I, Stan A-I, Teodorescu R, Kær SK (2015) Lifetime estimation of the nanophosphate LiFePO4/C battery chemistry used in fully electric vehicle. IEEE Trans Ind Appl. https://doi.org/10.1109/TIA.2015.2405500

    Article  Google Scholar 

  19. Xu B, Oudalov A, Poland J, Ulbig A, Andersson G (2014) BESS control strategies for participating in grid frequency regulation. In: 19th world congress of the international federation of automatic control, Cape Town, South Africa, https://doi.org/10.3182/20140824-6-ZA-1003.02148

  20. Shen J, Dusmez S (2014) Optimization of sizing and battery cycle life in battery/ultracapacitor hybrid energy storage systems for electric vehicle applications. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2014.2334233

    Article  Google Scholar 

  21. Lawder MT, Suthar B (2014) Battery energy storage system (BESS) and battery management system (BMS) for grid-scale applications. Proc IEEE. https://doi.org/10.1109/JPROC.2014.2317451

    Article  Google Scholar 

  22. Liu D, Xie W, Liao H (2015) An integrated probabilistic approach to lithium-ion battery remaining useful life estimation. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2014.2348613

    Article  Google Scholar 

  23. Fatha JP, Dragicevica D, Bittela L (2019) Quantification of aging mechanisms and in homogeneity in cycled Lithium-ion cells by differential voltage analysis. J Energy Storage 25:100813. https://doi.org/10.1016/j.est.2019.100813I

    Article  Google Scholar 

  24. Jha S, Sen S, Tiwari M, Singh MK (2014) Control strategy for frequency regulation using battery energy storage with optimal utilization. In: IEEE 6th India international conference on power electronics https://doi.org/10.1109/IICPE.2014.7115796.

  25. Wang Q, Zhao X, Ye J, Qiujuan Su, Ping P, Sun J (2016) Thermal response of lithium-ion battery during charging and discharging under adiabatic conditions. J Therm Anal Calorim 124:417–428. https://doi.org/10.1007/s10973-015-5100-4

    Article  Google Scholar 

  26. GhassanZubi R-L (2018) Monica Carvalhob, GuzayPasaogluc, The lithium-ion battery: State of the art and future perspectives. Renew Sustain Energy Rev 89:292–308. https://doi.org/10.1016/j.rser.2018.03.002

    Article  Google Scholar 

  27. Xu B, Andersson G (2016) Modeling of lithium-ion battery degradation for cell life assessment. IEEE Trans Smart Grid. https://doi.org/10.1109/TSG.2016.2578950

    Article  Google Scholar 

  28. Guo S, Xiong R, Shen W, Sun F (2019) Aging investigation of an echelon internal heating method on a three electrode Lithium-ion cell at low temperatures. J Energy Storage. https://doi.org/10.1016/j.est.2019.100878

    Article  Google Scholar 

  29. Meng J et al (2018) An overview and comparison of online implementable SOC estimation methods for lithium-ion battery. IEEE Trans Ind Appl 54(2):1583–1591. https://doi.org/10.1109/OPTIM.2017.7975030

    Article  Google Scholar 

  30. Uno M, Kukita A (2015) Cycle life evaluation based on accelerated aging testing for lithium-ion capacitors as alternative to rechargeable batteries. IEEE Trans Industr Electron. https://doi.org/10.1109/TIE.2015.2504578

    Article  Google Scholar 

  31. Tripathy Y, McGordon A, Low CTJ (2018) A new consideration for validating battery performance at low ambient temperatures. Energies 11:2439. https://doi.org/10.3390/en11092439

    Article  Google Scholar 

  32. Rivera-Barrera JP, Muñoz-Galeano N, Sarmiento-Maldonado HO (2017) SoC estimation for lithium-ion batteries: review and future challenges. Electronics 6:102. https://doi.org/10.3390/electronics6040102

    Article  Google Scholar 

  33. Stroe DI, Swierczynski M, Kær SK, Teodorescu R (2017) A comprehensive study on the degradation of lithium-ion batteries during calendar ageing: the internal resistance increase. IEEE Trans Ind Appl. https://doi.org/10.1109/ECCE.2016.7854664

    Article  Google Scholar 

  34. Sarasketa-Zabala E, Gandiga I, Rodriguez-Martinez L, Villareal I (2014) Calendar ageing analysis of a LiFePO4/graphite cell with dynamic model validations: Towards realistic lifetime predictions. J Power Sources 272:45–57. https://doi.org/10.1016/j.jpowsour.2014.08.051

    Article  Google Scholar 

Download references

Acknowledgements

Authors are thankful to the management of POWERGRID for granting permission for presentation of this paper. Views expressed in the paper are of the authors only and need not necessarily be that of the organization in which they belong.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Mahesh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahesh, M., Bhaskar, D.V., Jisha, R.K. et al. Lifetime estimation of grid connected LiFePO4 battery energy storage systems. Electr Eng 104, 67–81 (2022). https://doi.org/10.1007/s00202-021-01371-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01371-w

Keywords

Navigation