Advertisement

Engineering with Computers

, Volume 35, Issue 1, pp 115–125 | Cite as

Crash analysis of lithium-ion batteries using finite element based neural search analytical models

  • V. Vijayaraghavan
  • Li Shui
  • Akhil Garg
  • Xiongbin Peng
  • Vikas Pratap Singh
Original Article
  • 148 Downloads

Abstract

The electric operated road vehicles are frequently powered by lithium ion batteries due to its low cost and ease of manufacturing. However, unforeseen impacts in road conditions can lead to fire hazard due to short circuiting of the battery pack. The impact strength of the battery pack can hence provide a key design input for manufacturing next generation batteries with a durable safe limit. In this work, a finite element based neural search approach is proposed for determining the effects of various uncertain phenomena on the strength of the battery. The approach combines the actual impact mechanics of battery as determined by the finite element model along with the high accuracy and robustness provided by neural search algorithm. The derived model is able to satisfactorily predict the variances in the mechanical strength even with slightest uncertainties in the phenomena which can affect the strength of the battery pack. It is anticipated that the proposed model will be of utmost importance in design of next generation safe and durable lithium ion battery packs.

Keywords

Neural networks Model design Finite element Uncertainties Battery packs Mechanical strength 

Notes

Acknowledgements

This study was also supported by Shantou University Scientific Research Funded Project (Grant No. NTF 16002).

References

  1. 1.
    Arora S, Shen W, Kapoor A (2016) Review of mechanical design and strategic placement technique of a robust battery pack for electric vehicles. Renew Sustain Energy Rev 60:1319–1331CrossRefGoogle Scholar
  2. 2.
    Brown S, Pyke D, Steenhof P (2010) Electric vehicles: The role and importance of standards in an emerging market. Energy Policy 38(7):3797–806CrossRefGoogle Scholar
  3. 3.
    Chen SC, Wan CC, Wang YY (2005) Thermal analysis of lithium-ion batteries. J Power Sour 140(1):111–24CrossRefGoogle Scholar
  4. 4.
    Cuma MU, Koroglu T (2015) A comprehensive review on estimation strategies used in hybrid and battery electric vehicles. Renew Sustain Energy Rev 42:517–31CrossRefGoogle Scholar
  5. 5.
    Dai Y, Luo Y, Chu W, Li K (2014) Optimum tyre force distribution for four-wheel-independent drive electric vehicle with active front steering. Int J Veh Des 65(4):336–59CrossRefGoogle Scholar
  6. 6.
    Dinger A, Martin R, Mosquet X, Rabl M, Rizoulis D, Russo M, Sticher G (2010) Batteries for electric cars: challenges, opportunities, and the outlook to 2020. Boston Consulting Group, San Francisco, USAGoogle Scholar
  7. 7.
    Du J, Ouyang M (2013) Review of electric vehicle technologies progress and development prospect in China. In: Electric Vehicle Symposium and Exhibition (EVS27), 2013 World. IEEE, Barcelona, Spain, pp 1–8.  https://doi.org/10.1109/EVS.2013.6914849
  8. 8.
    Dubarry M, Vuillaume N, Liaw BY (2008) From Li-ion single cell model to battery pack simulation. In control applications, 2008. CCA 2008. IEEE international conference on 2008 Sep 3. IEEE pp 708–713Google Scholar
  9. 9.
    Fotouhi A, Auger DJ, Propp K, Longo S, Wild M (2016) A review on electric vehicle battery modelling: from lithium-ion toward lithium–sulphur. Renew Sustain Energy Rev 56:1008–1021CrossRefGoogle Scholar
  10. 10.
    Gallardo-Lozano J, Romero-Cadaval E, Milanes-Montero MI, Guerrero-Martinez MA (2014) Battery equalization active methods. J Power Sour 246:934–49CrossRefGoogle Scholar
  11. 11.
    Gong X, Xiong R, Mi CC (2015 Mar) Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells. IEEE Trans Ind Appl 51(2):1872–1879CrossRefGoogle Scholar
  12. 12.
    Hanifah RA, Toha SF, Ahmad S (2015) Electric vehicle battery modelling and performance comparison in relation to range anxiety. Procedia Comput Sci 76:250–256CrossRefGoogle Scholar
  13. 13.
    Hilbe JM (2012) STATISTICA 7. Am Stat 61(1):91–94.  https://doi.org/10.1198/000313007X172998
  14. 14.
    Huo H, Zhang Q, Wang MQ, Streets DG, He K (2010) Environmental implication of electric vehicles in China. Environ Sci Technol 24(13):4856–4861 44(CrossRefGoogle Scholar
  15. 15.
    Jaguemont J, Boulon L, Dubé Y (2016) A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Appl Energy 164:99–114CrossRefGoogle Scholar
  16. 16.
    Barillas JK, Li J, Günther C, Danzer MA (2015) A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems. Appl Energy 155:455–462CrossRefGoogle Scholar
  17. 17.
    Nykvist B, Nilsson M (2015) Rapidly falling costs of battery packs for electric vehicles. Nat Clim Change 5(4):329–32CrossRefGoogle Scholar
  18. 18.
    Sun F, Xiong R, He H (2016) A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl Energy 162:1399–1409CrossRefGoogle Scholar
  19. 19.
    Sahraei E, Hill R, Wierzbicki T (2012) Calibration and finite element simulation of pouch lithium-ion batteries for mechanical integrity. J Power Sour 201:307–321CrossRefGoogle Scholar
  20. 20.
    Sahraei E, Meier J, Wierzbicki T (2014) Characterizing and modeling mechanical properties and onset of short circuit for three types of lithium-ion pouch cells. J Power Sour 247:503–516CrossRefGoogle Scholar
  21. 21.
    Tie SF, Tan CW (2013) A review of energy sources and energy management system in electric vehicles. Renew Sustain Energy Rev 20:82–102CrossRefGoogle Scholar
  22. 22.
    Xiong R, Sun F, Gong X, Gao C (2014) A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles. Appl Energy 113:1421–1433CrossRefGoogle Scholar
  23. 23.
    Wang XL, Han WQ, Chen H, Bai J, Tyson TA, Yu XQ, Wang XJ, Yang XQ (2011) Amorphous hierarchical porous GeO x as high-capacity anodes for Li ion batteries with very long cycling life. J Am Chem Soc 133(51):20692–20695CrossRefGoogle Scholar
  24. 24.
    Wang H, Fu L, Bi J (2011) CO2 and pollutant emissions from passenger cars in China. Energy Policy 39(5):3005–3011CrossRefGoogle Scholar
  25. 25.
    Wen X, Xiao C (2011) Electric vehicle key technology research in China. In: 2011 international aegean conference on electrical machines and power electronics and 2011 electromotion joint conference (ACEMP), 8 Sep 2011. IEEE, Istanbul, Turkey, pp 308–314.  https://doi.org/10.1109/ACEMP.2011.6490616
  26. 26.
    Wierzbicki T, Sahraei E (2013) Homogenized mechanical properties for the jellyroll of cylindrical Lithium-ion cells. J Power Sour 241:467–476CrossRefGoogle Scholar
  27. 27.
    Yarime M, Shiroyama H, Kuroki Y (2008) The strategies of the Japanese auto industry in developing hybrid and fuel cell vehicles. Making choices about hydrogen: transport issues for developing countries. United Nations University Press, USA, p 193Google Scholar
  28. 28.
    Dufo-López R, Lujano-Rojas JM, Bernal-Agustín JL (2014) Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems. Appl Energy 115:242–253CrossRefGoogle Scholar
  29. 29.
    Young K, Wang C, Wang LY, Strunz K (2013) Electric vehicle battery technologies. In: Electric vehicle integration into modern power networks. Springer, New York pp 15–56CrossRefGoogle Scholar
  30. 30.
    Vijayaraghavan V, Garg A, Gao L, Vijayaraghavan R, Lu G (2016) A finite element based data analytics approach for modeling turning process of Inconel 718 alloys. J Clean Prod 137:1619–1627CrossRefGoogle Scholar
  31. 31.
    Vijayaraghavan V, Garg A, Gao L, Vijayaraghavan R (2017) Finite element based physical chemical modeling of corrosion in magnesium alloys. Metals 7(3):83CrossRefGoogle Scholar
  32. 32.
    Vijayaraghavan V, Garg A, Gao L (2018) Fracture mechanics modelling of lithium-ion batteries under pinch torsion test. Measurement 114:382–389CrossRefGoogle Scholar
  33. 33.
    Huang Y, Gao L, Yi Z, Tai K, Kalita P, Prapainainar P, Garg A (2018) An application of evolutionary system identification algorithm in modelling of energy production system. Measurement 114:122–131CrossRefGoogle Scholar
  34. 34.
    Garg A, Vijayaraghavan V, Zhang J, Li S, Liang X (2017) Design of robust battery capacity model for electric vehicle by incorporation of uncertainties. Int J Energy Res 41(10):1436–1451CrossRefGoogle Scholar
  35. 35.
    Garg A, Shankhwar K, Jiang D et al (2016) An evolutionary framework in modelling of multi-output characteristics of the bone drilling process. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2632-x
  36. 36.
    Garg A, Li J, Hou J, Berretta C, Garg A (2017) A new computational approach for estimation of wilting point for green infrastructure. Measurement 111:351–358CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • V. Vijayaraghavan
    • 1
    • 2
  • Li Shui
    • 3
  • Akhil Garg
    • 3
  • Xiongbin Peng
    • 3
  • Vikas Pratap Singh
    • 4
  1. 1.School of Mechanical and Manufacturing EngineeringThe University of New South WalesSydneyAustralia
  2. 2.School of EngineeringMonash University MalaysiaBandar SunwayMalaysia
  3. 3.Intelligent Manufacturing Key Laboratory of Ministry of EducationShantou UniversityShantouChina
  4. 4.Department of MechanicalIndian Institute of Technology JodhpurJodhpurIndia

Personalised recommendations