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Design of Bionic Structure Parameters of Pure EV BPE Based on Proportional Conjugate Gradient Algorithm

  • Research Article-Mechanical Engineering
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Abstract

With the rapid development of pure electric vehicles, how to improve the range has become the focus of research. Due to the large shell mass of the battery pack shell (BPE), it is necessary to optimize its structure in turn. In this paper, the BPE model was reconstructed by improving the upper shell material and adding reinforcements following the direction of the leaf vein fibers of the Magnolia Grandiflora. A quasi-Monte Carlo method based on Sobol sequences and a Latin hypercube design with variance sensitivity analysis was used to determine the seven design variables. The 122 data sets were trained and predicted using the basic gradient descent algorithm combined with the conjugate direction method, and the predictions were compared with static mechanical simulations for sharp cornering conditions on bumpy roads. The results showed that the BPE weight reduction ratio was 19.5%, and the maximum stress reduction ratio was 27.49%, and the displacement reduction ratio was 29.29% respectively, which satisfied the material requirements. It had a 20.88% increase in first-order mode frequency, which effectively prevented resonance.

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References

  1. Zhili, D.; Boqiang, L.; Chunxu, G.: Development path of electric vehicles in China under environmental and energy security constraints. Resour. Conserv. Recycl. 143, 17–26 (2019). https://doi.org/10.1016/j.resconrec.2018.12.007

    Article  Google Scholar 

  2. Wen, Z.; Li, H.; Zhang, X.; Chi Kin Lee, J.; Xu, C.: Low-carbon policy options and scenario analysis on CO2 itigation potential in China’s transportation sector. Greenh. Gases Sci. Technol. 7(1), 40–52 (2017). https://doi.org/10.1002/ghg.1623

    Article  Google Scholar 

  3. Mayyas, A.; Omar, M.; Hayajneh, M.; Mayyas, A.R.: Vehicle’s lightweight design vs. electrification from life cycle assessment perspective. J. Clean. Prod. 167, 687–701 (2017). https://doi.org/10.1016/j.jclepro

    Article  Google Scholar 

  4. Mossali, E.; Gentilini, L.; Merati, G.; Colledani, M.: Methodology and application of electric vehicles battery packs redesign for circular economy. Procedia CIRP 91, 747–751 (2020). https://doi.org/10.1016/J.PROCIR.2020.01.139

    Article  Google Scholar 

  5. Ribu, L.I.; Hailin, W.A.N.G.; Dongsheng, W.U., et al.: A lightweight technology review on battery pack of electrical vehicles. Automobile Parts 07, 101–107 (2019). https://doi.org/10.19466/j.cnki.1674-1986.2019.07.026

    Article  Google Scholar 

  6. Xiong, Y.; Pan, Y.; Wu, L.; Liu, B.: Effective weight-reduction-and crashworthiness-analysis of a vehicle’s battery-pack system via orthogonal experimental design and response surface methodology. Eng. Fail. Anal. 128, 105635 (2021). https://doi.org/10.1016/j.engfailanal.2021.105635

    Article  Google Scholar 

  7. Zuo, S.; Yin, B.; Xu, Y.; Wu, X.; Li, Y.; Wang, J.: A simplified method of soft connected battery module for finite element method model of battery pack. Int. J. Energy Res. 45(7), 10546–10561 (2021). https://doi.org/10.1002/er.6543

    Article  Google Scholar 

  8. Xia, B.; Liu, F.; Xu, C., et al.: Experimental and simulation modal analysis of a prismatic battery module. Energies 13(8), 2046 (2020). https://doi.org/10.3390/en13082046

    Article  CAS  Google Scholar 

  9. Uerlich, R.; Ambikakumari Sanalkumar, K.; Bokelmann, T.; Vietor, T.: Finite element analysis considering packaging efficiency of innovative battery pack designs. Int. J. Crashworthiness 25(6), 664–679 (2020). https://doi.org/10.1080/13588265.2019.1632545

    Article  Google Scholar 

  10. Wierzbicki, T.; Sahraei, E.: Homogenized mechanical properties for the jellyroll of cylindrical Lithium-ion cells. J. Power Sources 241, 467–476 (2013). https://doi.org/10.1016/j.jpowsour.2013.04.135

    Article  CAS  ADS  Google Scholar 

  11. Hooper, J.M.; Marco, J.: Experimental modal analysis of lithium-ion pouch cells. J. Power Sources 285, 247–259 (2015). https://doi.org/10.1016/j.jpowsour.2015.03.098

    Article  CAS  ADS  Google Scholar 

  12. Niu, X.; Garg, A.; Goyal, A.; Simeone, A.; Bao, N.; Zhang, J.; Peng, X.: A coupled electrochemical-mechanical performance evaluation for safety design of lithium-ion batteries in electric vehicles: an integrated cell and system level approach. J. Clean. Prod. 222, 633–645 (2019). https://doi.org/10.1016/j.jclepro.2019.03.065

    Article  CAS  Google Scholar 

  13. Pan, Y.; Xiong, Y.; Dai, W.; Diao, K.; Wu, L.; Wang, J.: Crush and crash analysis of an automotive battery-pack enclosure for lightweight design. Int. J. Crashworthiness 27(2), 500–509 (2022). https://doi.org/10.1080/13588265.2020.1812253

    Article  Google Scholar 

  14. Shui, L.; Chen, F.; Garg, A.; Peng, X.; Bao, N.; Zhang, J.: Design optimization of battery pack enclosure for electric vehicle. Struct. Multidiscip. Optim. 58(1), 331–347 (2018). https://doi.org/10.1007/s00158-018-1901-y

    Article  Google Scholar 

  15. Li, W.; Garg, A.; Xiao, M.; Peng, X.; Le Phung, M.L.; Tran, V.M.; Gao, L.: Intelligent optimization methodology of battery pack for electric vehicles: a multidisciplinary perspective. Int. J. Energy Res. 44(12), 9686–9706 (2020). https://doi.org/10.1002/er.5600

    Article  Google Scholar 

  16. Li, Y.: Multi-objective optimization design for battery pack of electric vehicle based on neural network of radial basis function (RBF). J. Phys. Conf. Ser. 1684(1), 012156 (2020). https://doi.org/10.1088/1742-6596/1684/1/012156

    Article  Google Scholar 

  17. Shahin, M.E.; Yun, L.; Chin, C.M.M.; Gao, L.; Wang, C.T.; Niu, X.; Garg, A.: An application of genetic programming for lithium-ion battery pack enclosure design: modelling of mass, minimum natural frequency and maximum deformation case. In: IOP Conference Series: Earth and Environmental Science, vol. 268, No. 1, p. 012065. IOP Publishing (2019). https://doi.org/10.1088/1755-1315/268/1/012065

  18. Cheng, W.W.: Research on the lightweighting of electric vehicle battery pack structure based on precision casting technology. Master's thesis, Hefei University of Technology (2019). https://doi.org/10.27101/d.cnki.ghfgu.2019.000372

  19. Shaoqiang, X.; Weiwei, L.; Lin, L.: Research and optimization on crashworthiness of self-similar bionic multi-cell thin-walled tube. Mech. Sci. Technol. Aerosp. Eng. (2022). https://doi.org/10.13433/j.cnki.1003-8728.20200517

    Article  Google Scholar 

  20. Kexian, B.; Yan, K.; Bin, Y.; Kangping, F.; Zhipeng, H.; Yuepeng, X.; Kong, X.: Mass modeling and sensitivity analysis of lightweight hydraulic actuators for foot-operated robots. J. Mech. Eng. 24, 39–48+82 (2021). https://doi.org/10.3901/JME.2021.24.039

  21. Xiong, G.; Wu, X.; Qiu, F.-L.; Zuomi, Dong: Sensitivity analysis and optimal design of hydraulic static pile driver press box structure. Mod. Manuf. Eng. 01, 129–133 (2016). https://doi.org/10.16731/j.cnki.1671-3133.2016.01.025

    Article  Google Scholar 

  22. Wei, S.; Li, Y.; Gao, X.; Lee, K.Y.; Sun, L.: Multi-stage sensitivity analysis of distributed energy systems: a variance-based sobol method. J. Mod. Power Syst. Clean Energy 8(5), 895–905 (2020). https://doi.org/10.35833/MPCE.2020.000134

    Article  Google Scholar 

  23. Chen, T.C.; Han, D.J.; Au, F.T.; Tham, L.G.: Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate. In: Proceedings of the International Joint Conference on Neural Networks, 2003. vol. 3, pp. 1873–1878. IEEE. (2003)

  24. Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Neural Networks: Tricks of the Trade, pp. 437–478. Springer, Berlin, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Qiang, H.U.O.; Xi, L.I.U.; Chen, L.J.; Wu, Y.H.; Wu, H.Y.; Xie, J.P.; Qiu, G.Z.: Treatment of backwater in bauxite flotation plant and optimization by using Box-Behnken design. Trans. Nonferrous Met. Soc. China 29(4), 821–830 (2019). https://doi.org/10.1016/S1003-6326(19)64992-7

    Article  Google Scholar 

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Funding

This study was funded by [1.Open Fund project of Transportation Industry Key Laboratory of Vehicle Testing, Diagnosis and Maintenance Technology: “Structural optimization design of Vehicle Power Battery Pack”,JTZL2004; 2.A Project of Shandong Province Higher Educational Science and Technology Program, J18KA006].

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Authors and Affiliations

  1. Shandong Jianzhu University, Jinan, 250101, Shandong, China

    Yuanyuan Gao, Na Liu, Changqing Cui, Peng Liu & Chengnuo Wang

  2. Henan Rekon GENERAL Machinery Manufacture Co., Ltd., No. 98 Songcheng Road, Kaifeng Area, Henan Pilot Free Trade Zone, Kaifeng, China

    Changqing Cui

Authors
  1. Yuanyuan Gao
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  2. Na Liu
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  3. Changqing Cui
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  4. Peng Liu
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  5. Chengnuo Wang
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Correspondence to Na Liu.

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Gao, Y., Liu, N., Cui, C. et al. Design of Bionic Structure Parameters of Pure EV BPE Based on Proportional Conjugate Gradient Algorithm. Arab J Sci Eng 49, 1461–1477 (2024). https://doi.org/10.1007/s13369-023-07884-9

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  • DOI: https://doi.org/10.1007/s13369-023-07884-9

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