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Analysis of the critical failure modes and developing an aging assessment methodology for lithium iron phosphate batteries


Lithium-ion batteries are electrochemical storage devices that occupy an important place today in the field of renewable energy applications. However, challenging requirements of lithium-iron-phosphate LiFePO4 (LFP) batteries in terms of performances, safety and lifetime must to be met for increase their integrations in these applications. It is important to identify the origins and symptoms of battery aging and to quantify the various aging modes. In this context, the aim of this paper is to develop a reliability approach which ensures the evaluation of LFP batteries aging using causal tree analysis and electrical equivalent circuit model. The causal tree enables a good understanding of common aging modes such as the loss of lithium inventory, loss of active mass, electrolyte degradation and corrosion of the current collectors and a comprehensive identification of their potential causes. The determination of electric equivalent model parameters is based on experimental voltage responses under different discharge rates and operating temperatures. This experimental study allows creating database parameters of a new battery, which takes into account the temperature effect using the fuzzy logic method. The proposed diagnostic system is based on the use of electrochemical impedance spectroscopy (EIS) technique to determine the parameters of used battery and compared them to the parameters of new battery. The comparison results allow quantifying the depth of the various aging modes as well as the number of remaining cycles. This study is completed by testing three LFP batteries that give insights into the feasibility and efficiency of the proposed diagnostic method.

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  1. 1.

    Fuengwarodsakul NH (2016) Battery management system with active inrush current control for Li-ion battery in light electric vehicles. Electr Eng 98:17–27.

    Article  Google Scholar 

  2. 2.

    Yang LY, Ai OC, Hamidi MN (2020) Performance evaluation of grid-connected power conversion systems integrated with real-time battery monitoring in a battery energy storage system. Electr Eng 102:245–258.

    Article  Google Scholar 

  3. 3.

    Han X, Lu L, Zheng Y, Feng X, Li Z, Li J, Ouyang M (2019) A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation 1:100005.

    Article  Google Scholar 

  4. 4.

    Azzouz I, Hammami I, Brik K, Ben Ammar F (2020) Criticality assessment with Pareto diagram of the different solar batteries technologies. 17th International Multi-Conference on Systems, Signals & Devices (SSD), 513–518

  5. 5.

    Vetter J, Novak P, Wagner MR, Veit C, Moller KC, Besenhard JO, Winter M, Wohlfahrt-Mehrens M, Vogler C, Hammouche A (2005) Ageing mechanisms in lithium-ion batteries. J Power Sour 147:269–281.

    Article  Google Scholar 

  6. 6.

    Wu C, Zhu C, Ge Y, Zhao Y (2015) A Review on fault mechanism and diagnosis approach for Li-Ion batteries. J Nanomater.

    Article  Google Scholar 

  7. 7.

    Lin C, Tang A, Mu H, Wang W, Wang C (2015) Aging mechanisms of electrode materials in Lithium-Ion batteries for electric vehicles. J Chem.

    Article  Google Scholar 

  8. 8.

    Omar N, Abdel MM, Firouz Y, Salminen J, Smekens J, Hegazy O, Gaulous H, Mulder G, Van den Bossche P, Coosemans T, Van Mierlo J (2014) Lithium iron phosphate based battery–Assessment of the aging parameters and development of cycle life model. Appl Energy 113:1575–1585.

    Article  Google Scholar 

  9. 9.

    Beltran H, Ayuso P, Pérez E (2020) Lifetime expectancy of Li-Ion batteries used for residential solar storage. Energies 13(3):568.

    Article  Google Scholar 

  10. 10.

    Stroe DI, Swierczynski M, Stan AI, Knap V, Teodorescu R, Andreasen SJ (2014) Diagnosis of Lithium-Ion batteries State-of-Health based on electrochemical impedance spectroscopy technique. IEEE ECCE.

    Article  Google Scholar 

  11. 11.

    She C, Wang Z, Sun F, Liu P, Zhang L (2020) Battery aging assessment for real-world electric buses based on incremental Capacity analysis and radial basis function neural network. IEEE Trans Industr Inf 16:3345–3354.

    Article  Google Scholar 

  12. 12.

    Xiong R, Wang J, Shen W, Tian J, Mu H (2021) Co-estimation of State of charge and capacity for Lithium-ion batteries with multi-stage model fusion method. Engineering.

    Article  Google Scholar 

  13. 13.

    Henschel J, Horsthemke F, Philipp SY, Evertz M, Kösters K, Wiemers-Meyer S, Winter M, Nowak S (2020) Lithium ion battery electrolyte degradation of field-tested electric vehicle battery cells–A comprehensive analytical study. J Power Sources 447:227370.

    Article  Google Scholar 

  14. 14.

    Hendricks C, Williard N, Mathew S, Pecht M (2015) A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries. J Power Sour 297:113–120.

    Article  Google Scholar 

  15. 15.

    Wang Q, Wang Z, Zhang L, Liu P, Zhang Z (2020) A novel consistency evaluation method for series-connected battery systems based on real-world operation data. IEEE Trans Transp Electr.

    Article  Google Scholar 

  16. 16.

    Birkl CR, Roberts MR, McTurk E, Bruce PG, Howey DA (2017) Degradation diagnostics for lithium ion cells. J Power Sour 341:373–386.

    Article  Google Scholar 

  17. 17.

    Pastor-Fernández C, Uddin K, Chouchelamane GH, Dhammika WW, Marco J (2017) A Comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems. J Power Sour 360:301–318.

    Article  Google Scholar 

  18. 18.

    Agubra V, Fergus J (2013) Lithium Ion battery anode aging mechanisms. Materials 6(4):1310–1325.

    Article  Google Scholar 

  19. 19.

    Miao Y, Hynan P, Jouanne A, Yokochi A (2019) Current Li-Ion battery technologies in electric vehicles and opportunities for advancements. Energies 12(6):1074.

    Article  Google Scholar 

  20. 20.

    Wikner E, Thiringer T (2018) Extending battery lifetime by avoiding high SOC. Appl Sci 8(10):1825.

    Article  Google Scholar 

  21. 21.

    Zhu J, Sun Z, Wei X, Dai H (2017) Battery internal temperature estimation for LiFePO4 battery based on impedance phase shift UNDER operating conditions. Energies 10(1):60.

    Article  Google Scholar 

  22. 22.

    Beletskii EV, Alekseeva EV, Spiridonova DV, Yankin AN, Levin OV (2019) Overcharge cycling effect on the surface layers and crystalline structure of LiFePO4 cathodes of Li-Ion batteries. Energies 12(24):4652.

    Article  Google Scholar 

  23. 23.

    Preger Y, Barkholtz HM, Fresquez A, Campbell DL, Juba BW, Romàn-Kustas J, Ferreira SR, Chalamala B (2020) Degradation of commercial Lithium-Ion cells as a function of chemistry and cycling conditions. J Electrochem Soc 167:120532.

    Article  Google Scholar 

  24. 24.

    Yuan L, Wang Z, Zhang W, Hu X, Chen J, Huang Y, Goodenough JB (2011) Development and challenges of LiFePO4 cathode material for lithium-ion batteries. Energy Environ Sci 4:269–284.

    Article  Google Scholar 

  25. 25.

    Kabir MM, Demirocak DE (2017) Degradation mechanisms in Li-ion batteries: a state-of-the-art review. Int J Energy Res 41(14):1963–1986.

    Article  Google Scholar 

  26. 26.

    Wojciechowski J, Kolanowski Ł, Bund A, Lota G (2017) The influence of current collector corrosion on the performance of electrochemical capacitors. J Power Sour 368:18–29.

    Article  Google Scholar 

  27. 27.

    Tomaszewska A, Chu Z, Feng X, O’Kane S, Liu X, Chen J, Ji C, Endler E, Li R, Liu L, Li Y, Zheng S, Vetterlein S, Gao M, Du J, Parkes M, Ouyang M, Marinescu M, Wu B (2019) Lithium-ion battery fast charging: a review. TeTransportation. 1:100011.

    Article  Google Scholar 

  28. 28.

    Camacho-Forero LE, Balbuena PB (2020) Effects of charged interfaces on electrolyte decomposition at the lithium metal anode. J Power Sour 472:228449.

    Article  Google Scholar 

  29. 29.

    Rosenberg E, Kanakaki C, Amon A, Gocheva I, Trifonova A (2017) Understanding the degradation processes of the electrolyte of lithium ion batteries by chromatographic analysis. Bulg Chem Commun 49:242–253

    Google Scholar 

  30. 30.

    Brik K, Ben Ammar F, Djerdir A, Miraoui A (2015) Causal and fault Trees analysis of proton exchange membrane fuel cell degradation. J Fuel Cell Sci Technol 12(5):051002.

    Article  Google Scholar 

  31. 31.

    Yahmadi R, Brik K, Ben AF (2018) Causal tree analysis for quality control of the lead acid battery manufacturing process. Int J Energy Res 2018:1–22.

    Article  Google Scholar 

  32. 32.

    Saeed MS, Schaltz E, Knudsen KS (2019) An electrical equivalent circuit model of a Lithium Titanate Oxide battery. Batteries 5(1):31.

    Article  Google Scholar 

  33. 33.

    Yu Y, Narayan N, Vega-Garita V, Popovic-Gerber J, Qin Z, Wagemaker M, Bauer P, Zeman M (2018) Constructing accurate equivalent electrical circuit models of Lithium Iron phosphate and Lead-Acid battery cells for solar home system applications. Energy (Oxf) 11(9):2305.

    Article  Google Scholar 

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Correspondence to Raja Yahmadi.

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Azzouz, I., Yahmadi, R., Brik, K. et al. Analysis of the critical failure modes and developing an aging assessment methodology for lithium iron phosphate batteries. Electr Eng (2021).

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  • LFP batteries
  • Causal tree
  • Aging mode
  • Fuzzy logic method
  • Diagnostic
  • Renaiming number cycle