There is a newer version of the record available.

Published November 3, 2026 | Version v1.0

Designing the side frame of an electric-vehicle battery enclosure for oblique pole impact

  • 1. Universitas Negeri Makassar
  • 2. Universitas Hasanuddin Fakultas Teknik

Description

The side frame of an electric-vehicle battery enclosure must absorb pole-impact energy before the battery modules are damaged. Enclosure optimisation studies usually limit intrusion into the cell zone and so treat any contact between frame and modules as failure. This study examines how that choice changes the optimal frame. A subsystem model of an enclosure section under a 32 km/h, 75° oblique pole impact was built in the open-source explicit solver OpenCourant. The aluminium model was validated against axial-crush tests on AA6063-T5 extrusions (mean crushing force and absorbed energy within 9%), and the modules were represented by homogenised blocks calibrated against published module indentation tests. Frame wall thickness, number of frame cells and tray and cross-member thicknesses were varied in 61 simulations, combined with Kriging surrogates and NSGA-II. Under the no-contact criterion a single-cell frame was lightest, but by no more than 3%. Under a damage criterion of 5 mm module indentation the ranking reversed: the single-cell frames absorbed 16–23% less energy before module damage, and a two-cell frame with 3.2 mm walls raised this energy by 28% for 12% more mass. Frames whose peak force before module contact exceeded about 130 kN damaged the modules earlier, so their energy fell although their mass rose. This stiffness limit persisted when the module strength was varied by ±33%. Tray and cross-member thickness changed the result by less than 4%. All input decks and scripts are openly released

Files

ev-enclosure-pole-impact_zenodo.zip

Files (3.9 MB)

Name Size
md5:f2e00b381614e77f5fcc9f45176196a8
3.9 MB Preview Download

Additional details

Additional titles

Alternative title (English)
a module-damage criterion reverses the optimal topology and reveals a stiffness limit

References

  • 1. Luk JM, Kim HC, De Kleine R, et al. Review of the fuel saving, life cycle GHG emission, and ownership cost impacts of lightweighting vehicles with different powertrains. Environ Sci Technol 2017;51:8215–28. doi:10.1021/acs.est.7b00909
  • 2. Del Pero F, Berzi L, Antonacci A, et al. Automotive lightweight design: simulation modeling of mass-related consumption for electric vehicles. Machines 2020;8(3):51
  • 3. Zhang W, Xu J. Advanced lightweight materials for automobiles: a review. Mater Des 2022;221:110994. doi:10.1016/j.matdes.2022.110994
  • 4. Czerwinski F. Current trends in automotive lightweighting strategies and materials. Materials 2021;14:6631. doi:10.3390/ma14216631
  • 5. Löbberding H, Wessel S, Offermanns C, et al. From cell to battery system in BEVs: analysis of system packing efficiency and cell types. World Electr Veh J 2020;11:77. doi:10.3390/wevj11040077
  • 6. Arora S, Shen W, Kapoor A. Review of mechanical design and strategic placement technique of a robust battery pack for electric vehicles. Renew Sustain Energy Rev 2016;60:1319–31. doi:10.1016/j.rser.2016.03.013
  • 7. Zhu J, Wierzbicki T, Li W. A review of safety-focused mechanical modeling of commercial lithium-ion batteries. J Power Sources 2018;378:153–68. doi:10.1016/j.jpowsour.2017.12.034
  • 8. Liu B, Jia Y, Yuan C, et al. Safety issues and mechanisms of lithium-ion battery cell upon mechanical abusive loading: a review. Energy Storage Mater 2020;24:85–112. doi:10.1016/j.ensm.2019.06.036
  • 9. Deng J, Bae C, Marcicki J, et al. Safety modelling and testing of lithium-ion batteries in electrified vehicles. Nat Energy 2018;3:261–6. doi:10.1038/s41560-018-0122-3
  • 10. UNECE. Regulation No. 135 – Uniform provisions concerning the approval of vehicles with regard to their Pole Side Impact performance (PSI). 2015, as amended
  • 11. Euro NCAP. Oblique Pole Side Impact Testing Protocol v7.2, 2023; Crash Protection – Side Impact Protocol v1.0, 2025
  • 12. UNECE. Regulation No. 100 – Uniform provisions concerning the approval of vehicles with regard to specific requirements for the electric power train. OJ EU L 87/1, 31.3.2015. 13. SAMR/SAC. GB 38031-2025 Electric vehicles traction battery safety requirements (replaces GB 38031-2020).
  • 13. SAMR/SAC. GB 38031-2025 Electric vehicles traction battery safety requirements (replaces GB 38031-2020).
  • 14. Shui L, Chen F, Garg A, et al. Design optimization of battery pack enclosure for electric vehicle. Struct Multidiscip Optim 2018;58:331–47. doi:10.1007/s00158-018-1901-y
  • 15. Pan Y, Xiong Y, Wu L, et al. Lightweight design of an automotive battery-pack enclosure via advanced high-strength steels and size optimization. Int J Automot Technol 2021;22:1279–90. doi:10.1007/s12239-021-0112-5
  • 16. Kawsar I, Liu C, Cheng D, et al. Enhancing crashworthiness performance of a battery pack system using multicell square tube structures. Eur J Mech A Solids 2025;112:105665. doi:10.1016/j.euromechsol.2025.105665
  • 17. Zhang D, Sun Z, Zhang H, et al. Manufacturability-constrained multi-objective optimization of an EV battery pack enclosure for side-pole impact. World Electr Veh J 2025;16:632. doi:10.3390/wevj16110632
  • 18. Yay I, Demirci E, Ozcan AR. Multi-objective optimization of aluminum foam-filled battery boxes for electric vehicle safety. Lat Am J Solids Struct 2025;22(3):e8408. doi:10.1590/1679-7825/e8408
  • 19. Wang W, Dai S, Zhao W, et al. Design optimization of a novel negative Poisson's ratio non-module battery pack system considering crashworthiness and heat dissipation. Compos Struct 2021;275:114458. doi:10.1016/j.compstruct.2021.114458
  • 20. Xu D, Pan Y, Zhang X, et al. Data-driven modelling and evaluation of a battery-pack system's mechanical safety against bottom cone impact. Energy 2024;290:130145. doi:10.1016/j.energy.2023.130145
  • 21. Nasrullah AIH, Santosa SP, Widagdo D, et al. Structural lattice topology and material optimization for battery protection in EVs subjected to ground impact using ANN and GA. Materials 2021;14:7618. doi:10.3390/ma14247618
  • 22. Kulkarni SS, Hale F, Taufique MFN, et al. Investigation of crashworthiness of carbon fiber-based EV battery enclosure using finite element analysis. Appl Compos Mater 2023;30:1689–715. doi:10.1007/s10443-023-10146-4
  • 23. Shaikh SA, Taufique MFN, Balusu K, et al. Finite element analysis and machine learning guided design of carbon fiber organosheet-based battery enclosures for crashworthiness. Appl Compos Mater 2024;31:1475–93. doi:10.1007/s10443-024-10218-z
  • 24. Ma Q, Dong F, Qin X, et al. Parallel optimization of design and manufacturing: carbon fiber battery pack for electric vehicles. Mech Adv Mater Struct 2024;31:2289–306. doi:10.1080/15376494.2022.2155273
  • 25. Zhang X, Lin Q, Xiao Y, et al. Top-down design approach of lightweight composite battery pack enclosure for EVs based on numerical modeling and topology optimization. Polymers 2025;17:2897. doi:10.3390/polym17212897
  • 26. Baroutaji A, Sajjia M, Olabi AG. On the crashworthiness performance of thin-walled energy absorbers: recent advances and future developments. Thin-Walled Struct 2017;118:137–63. doi:10.1016/j.tws.2017.05.018
  • 27. Fang J, Sun G, Qiu N, et al. On design optimization for structural crashworthiness and its state of the art. Struct Multidiscip Optim 2017;55:1091–119. doi:10.1007/s00158-016-1579-y
  • 28. Yao R, Pang T, Zhang B, et al. On the crashworthiness of thin-walled multi-cell structures and materials: state of the art and prospects. Thin-Walled Struct 2023;189:110734. doi:10.1016/j.tws.2023.110734
  • 29. Albak EI. Crashworthiness design for multi-cell circumferentially corrugated thin-walled tubes with sub-sections under multiple loading conditions. Thin-Walled Struct 2021;164:107886. doi:10.1016/j.tws.2021.107886
  • 30. Isaac CW, Ezekwem C. A review of the crashworthiness performance of energy absorbing composite structure within the context of materials, manufacturing and maintenance for sustainability. Compos Struct 2021;257:113081. doi:10.1016/j.compstruct.2020.113081
  • 31. Logakannan KP, Zhu F, Sypeck D, et al. Testing and modeling of vehicle Li-ion battery module with prismatic cells under abuse conditions. Energies 2023;16:1055. doi:10.3390/en16031055 32. Kisters T, Sahraei E, Wierzbicki T. Dynamic impact tests on lithium-ion cells. Int J Impact Eng 2017;108:205–16. doi:10.1016/j.ijimpeng.2017.04.025
  • 32. Kisters T, Sahraei E, Wierzbicki T. Dynamic impact tests on lithium-ion cells. Int J Impact Eng 2017;108:205–16. doi:10.1016/j.ijimpeng.2017.04.025
  • 33. Zhang X, Sahraei E, Wang K. Li-ion battery separators, mechanical integrity and failure mechanisms leading to soft and hard internal shorts. Sci Rep 2016;6:32578. doi:10.1038/srep32578
  • 34. Song Y, Bulla M, Patanwala H, et al. Validation of Sahraei failure criterion on cylindrical and pouch lithium-ion battery cells. J Energy Storage 2024;94:112371. doi:10.1016/j.est.2024.112371
  • 35. Wang G, Guo X, Chen J, et al. Safety performance and failure criteria of lithium-ion batteries under mechanical abuse. Energies 2023;16:6346. doi:10.3390/en16176346 † 36. Bulla M, Schmandt C, Kolling S, et al. An experimental and numerical study on charged 21700 lithium-ion battery cells under dynamic and high mechanical loads. Energies 2023;16(1):211. doi:10.3390/en16010211
  • 36. Bulla M, Schmandt C, Kolling S, et al. An experimental and numerical study on charged 21700 lithium-ion battery cells under dynamic and high mechanical loads. Energies 2023;16(1):211. doi:10.3390/en16010211
  • 37. Elrefaie M, Ahmed F, Shu D, et al. CarCrashNet: a large-scale dataset and hierarchical neural solver for data-driven structural crash simulation. arXiv:2605.07098, 2026 (preprint)
  • 38. OpenCourant (community continuation of OpenRadioss, originally released by Altair Engineering). GNU AGPL v3. github.com/OpenCourant/OpenCourant; opencourant.org (Windows 64-bit build of 11 October 2026, based on OpenRadioss commit 1a0d691, tag openradioss-final, 28 September 2026).
  • 39. Costas M, Morin D, Langseth M, et al. Axial crushing of aluminum extrusions filled with PET foam and GFRP: an experimental investigation. Thin-Walled Struct 2016;99:45–57. doi:10.1016/j.tws.2015.11.003
  • 40. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002;6(2):182–97. doi:10.1109/4235.996017 41. Blank J, Deb K. pymoo: multi-objective optimization in Python. IEEE Access 2020;8:89497–509. doi:10.1109/ACCESS.2020.2990567
  • 41. Blank J, Deb K. pymoo: multi-objective optimization in Python. IEEE Access 2020;8:89497–509. doi:10.1109/ACCESS.2020.2990567
  • 42. Mert SK, Demiral M, Altin M, et al. Experimental and numerical investigation on the crashworthiness optimization of thin-walled aluminum tubes considering damage criteria. J Braz Soc Mech Sci Eng 2021;43(2). doi:10.1007/s40430-020-02793-6