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Safety modeling and protection for lithium-ion batteries based on artificial neural networks method under mechanical abuse

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Abstract

The safety of lithium-ion batteries under mechanical abuse has become one of the major obstacles affecting the development of electric vehicles. In this paper, the lithium-ion battery safety model under mechanical abuse conditions is proposed by the Back Propagation Artificial Neural Network (BP-ANN) optimized by the Genetic Algorithm (GA). By experimental and simulation results, the proposed method can effectively predict battery mechanical properties. The corresponding correlation coefficient is greater than 0.99, the failure warning model has more safety margin, and the average security margin is greater than 29%. The multi-source warning weight shows that the mechanical soft short-circuit has the greatest warning margin, followed by that of the soft short-circuit of the electrical signal. The thermal soft short-circuit has the lowest warning margin because of the low thermal conductivity. The qualitative simulation results of the battery module reveal that, when electric vehicles are subjected to mechanical abuse conditions, the rapid reduction in the state-of-charge (SOC) of the rear batteries can effectively increase the reliability of the battery module. The proposed safety model is important to protect the safety and stability of lithium-ion batteries, which is conducive to promoting new energy vehicles and protecting the environment.

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References

  1. He X M, Feng X N, Ouyang M G. Safety of lithium-ion power battery system for vehicle (in Chinese). Sci Tech Rep, 2016, 34: 32–38

    Google Scholar 

  2. Chen Z Y, Xiong R, Sun F C. Research status and analysis for battery safety accidents in electric vehicles (in Chinese). J Mech Eng, 2019, 55: 93–116

    Article  Google Scholar 

  3. China Automotive Technology and Research Center. C-NCAP Management Regulation (2018 edition). http://www.c-ncap.org/cms/files/cncap-regulation-2018-en.pdf

  4. Wierzbicki T, Sahraei E. Homogenized mechanical properties for the jellyroll of cylindrical lithium-ion cells. J Power Sources, 2013, 241: 467–476

    Article  Google Scholar 

  5. Avdeev I, Gilaki M. Structural analysis and experimental characterization of cylindrical lithium-ion battery cells subject to lateral impact. J Power Sources, 2014, 271: 382–391

    Article  Google Scholar 

  6. Wang W W, Yang S, Lin C. Clay-like mechanical properties for the jellyroll of cylindrical Lithium-ion cells. Appl Energy, 2017, 196: 249–258

    Article  Google Scholar 

  7. Sahraei E, Meier J, Wierzbicki T. Characterizing and modeling mechanical properties and onset of short circuit for three types of lithium-ion pouch cells. J Power Sources, 2014, 247: 503–516

    Article  Google Scholar 

  8. Sahraei E, Campbell J, Wierzbicki T. Modeling and short circuit detection of 18650 Li-ion cells under mechanical abuse conditions. J Power Sources, 2012, 220: 360–372

    Article  Google Scholar 

  9. Xu J, Liu B H, Wang L B, et al. Dynamic mechanical integrity of cylindrical lithium-ion battery cell upon crushing. Eng Failure Anal, 2015, 53: 97–110

    Article  Google Scholar 

  10. Oh K Y, Samad N A, Kim Y, et al. A novel phenomenological multi-physics model of Li-ion battery cells. J Power Sources, 2016, 326: 447–458

    Article  Google Scholar 

  11. Oh K Y, Epureanu B I, Siegel J B, et al. Phenomenological force and swelling models for rechargeable lithium-ion battery cells. J Power Sources, 2016, 310: 118–129

    Article  Google Scholar 

  12. Wang W W, Li Y D, Lin C, et al. State of charge-dependent failure prediction model for cylindrical lithium-ion batteries under mechanical abuse. Appl Energy, 2019, 251: 113365

    Article  Google Scholar 

  13. Wang W W, Li Y D, Lin C, et al. Mass-spring-damping theory based equivalent mechanical model for cylindrical lithium-ion batteries under mechanical abuse. Chin J Mech Eng, 2020, 33: 23

    Article  Google Scholar 

  14. Li W, Zhu J E, Xia Y, et al. Data-driven safety envelope of lithium-ion batteries for electric vehicles. Joule, 2019, 3: 2703–2715

    Article  Google Scholar 

  15. Chen C, Zuo Y X, Ye W K, et al. A critical review of machine learning of energy materials. Adv Energy Mater, 2020, 10: 1903242

    Article  Google Scholar 

  16. Liu Y, Guo B R, Zou X X, et al. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater, 2020, 31: 434–450

    Article  Google Scholar 

  17. Liu Y, Wu J M, Avdeev M, et al. Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties. Adv Theor Simul, 2020, 3: 1900215

    Article  Google Scholar 

  18. Shi S Q, Gao J, Liu Y, et al. Multi-scale computation methods: Their applications in lithium-ion battery research and development. Chin Phys B, 2016, 25: 018212

    Article  Google Scholar 

  19. Zhang C, Xu J, Cao L, et al. Constitutive behavior and progressive mechanical failure of electrodes in lithium-ion batteries. J Power Sources, 2017, 357: 126–137

    Article  Google Scholar 

  20. Zhu J E, Zhang X W, Sahraei E, et al. Deformation and failure mechanisms of 18650 battery cells under axial compression. J Power Sources, 2016, 336: 332–340

    Article  Google Scholar 

  21. Sahraei E, Bosco E, Dixon B, et al. Microscale failure mechanisms leading to internal short circuit in Li-ion batteries under complex loading scenarios. J Power Sources, 2016, 319: 56–65

    Article  Google Scholar 

  22. Raffler M, Sevarin A, Ellersdorfer C, et al. Finite element model approach of a cylindrical lithium ion battery cell with a focus on minimization of the computational effort and short circuit prediction. J Power Sources, 2017, 360: 605–617

    Article  Google Scholar 

  23. Zhang C, Santhanagopalan S, Sprague M A, et al. Coupled mechanical-electricalthermal modeling for short-circuit prediction in a lithium-ion cell under mechanical abuse. J Power Sources, 2015, 290: 102–113

    Article  Google Scholar 

  24. Zhang C, Santhanagopalan S, Sprague M A, et al. A representative-sandwich model for simultaneously coupled mechanical-electricalthermal simulation of a lithium-ion cell under quasi-static indentation tests. J Power Sources, 2015, 298: 309–321

    Article  Google Scholar 

  25. Greve L, Fehrenbach C. Mechanical testing and macro-mechanical finite element simulation of the deformation, fracture, and short circuit initiation of cylindrical Lithium ion battery cells. J Power Sources, 2012, 214: 377–385

    Article  Google Scholar 

  26. Sahraei E, Hill R, Wierzbicki T. Calibration and finite element simulation of pouch lithium-ion batteries for mechanical integrity. J Power Sources, 2012, 201: 307–321

    Article  Google Scholar 

  27. Luo H L, Xia Y, Zhou Q. Mechanical damage in a lithium-ion pouch cell under indentation loads. J Power Sources, 2017, 357: 61–70

    Article  Google Scholar 

  28. Meng X Q, Xu Y L, Cao H B, et al. Internal failure of anode materials for lithium batteries—A critical review. Green Energy Environ, 2020, 5: 22–36

    Article  Google Scholar 

  29. Sazhin S V, Dufek E J, Gering K L. Enhancing Li-ion battery safety by early detection of nascent internal shorts. J Electrochem Soc, 2017, 164: A6281–A6287

    Article  Google Scholar 

  30. Nascimento M, Novais S, Ding M S, et al. Internal strain and temperature discrimination with optical fiber hybrid sensors in Li-ion batteries. J Power Sources, 2019, 410–411: 1–9

    Article  Google Scholar 

  31. Nascimento M, Ferreira M S, Pinto J L. Real time thermal monitoring of lithium batteries with fiber sensors and thermocouples: A comparative study. Measurement, 2017, 111: 260–263

    Article  Google Scholar 

  32. Peng J, Zhou X, Jia S, et al. High precision strain monitoring for lithium ion batteries based on fiber Bragg grating sensors. J Power Sources, 2019, 433: 226692

    Article  Google Scholar 

  33. Wang W W, Li Y D, Cheng L, et al. Safety performance and failure prediction model of cylindrical lithium-ion battery. J Power Sources, 2020, 451: 227755

    Article  Google Scholar 

  34. Kim G H, Smith K, Ireland J, et al. Fail-safe design for large capacity lithium-ion battery systems. J Power Sources, 2012, 210: 243–253

    Article  Google Scholar 

  35. Zhang M X, Du J Y, Liu L S, et al. Internal short circuit detection method for battery pack based on circuit topology. Sci China Tech Sci, 2018, 61: 1502–1511

    Article  Google Scholar 

  36. Ichimura M. The safety characteristics of lithium-ion batteries for mobile phones and the nail penetration test. In: Proceedings of the 29th International Telecommunications Energy Conference INTELEC. Rome: IEEE, 2007. 687–692

    Chapter  Google Scholar 

  37. Mao B B, Chen H D, Cui Z X, et al. Failure mechanism of the lithium ion battery during nail penetration. Int J Heat Mass Transfer, 2018, 122: 1103–1115

    Article  Google Scholar 

  38. Han W J, Zou C F, Zhou C, et al. Estimation of cell SOC evolution and system performance in module-based battery charge equalization systems. IEEE Trans Smart Grid, 2019, 10: 4717–4728

    Article  Google Scholar 

  39. Xu J, Liu B H, Hu D Y. State of charge dependent mechanical integrity behavior of 18650 lithium-ion batteries. Sci Rep, 2016, 6: 21829

    Article  Google Scholar 

  40. Xu J, Liu B H, Wang X Y, et al. Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies. Appl Energy, 2016, 172: 180–189

    Article  Google Scholar 

  41. Yang S, Wang W W, Lin C, et al. Improved constitutive model of the jellyroll for cylindrical lithium ion batteries considering microscopic damage. Energy, 2019, 185: 202–212

    Article  Google Scholar 

  42. Han W, Wik T, Kersten A, et al. Next-generation battery management systems: Dynamic reconfiguration. EEE Ind Electron Mag, 2020, 14: 20–31

    Article  Google Scholar 

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Authors and Affiliations

  1. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, 100081, China

    YiDing Li, WenWei Wang, Cheng Lin & FengHao Zuo

  2. Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, 518118, China

    WenWei Wang

Authors
  1. YiDing Li
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  2. WenWei Wang
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  3. Cheng Lin
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  4. FengHao Zuo
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Corresponding author

Correspondence to WenWei Wang.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No. 52072039), and the Key R&D Program of Beijing (Grant No. Z181100004518005).

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Li, Y., Wang, W., Lin, C. et al. Safety modeling and protection for lithium-ion batteries based on artificial neural networks method under mechanical abuse. Sci. China Technol. Sci. 64, 2373–2388 (2021). https://doi.org/10.1007/s11431-021-1826-2

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  • DOI: https://doi.org/10.1007/s11431-021-1826-2

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