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Design Optimization of Auxetic Structure for Crashworthy Pouch Battery Protection Using Machine Learning Method

Author

Listed:
  • Farras Ezra Carakapurwa

    (Lightweight Structure Laboratory, Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung (ITB), Jalan Ganesha 10, Bandung 40132, Indonesia)

  • Sigit Puji Santosa

    (Lightweight Structure Laboratory, Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung (ITB), Jalan Ganesha 10, Bandung 40132, Indonesia
    National Center of Sustainable Transportation Technology (NCSTT), Jalan Ganesha 10, Bandung 40132, Indonesia)

Abstract

In 2021, the electric vehicles (EVs) market reached a record-breaking 6.5 million vehicles, and it will continuously grow to USD 31 million in 2030. However, the risk of battery damage should be reduced using a lightweight crashworthy protection system, which can be performed through design optimization to achieve maximum Specific Energy Absorption (SEA). Maximum SEA can be gained by selecting a material with a light weight and high energy absorption properties. An auxetic-shaped cell structure was used since its negative Poisson ratio yields better energy absorption. The research was performed by varying the auxetic cell shape (Re-entrant, Double Arrow, Star-shaped, Double-U), material selection (GFRP, CFRP, aluminum, carbon steel), and geometry variables until the maximum possible SEA was reached. The Finite Element Method (FEM) was used to simulate the impact and obtain the value of the SEA of the varied auxetic cellular structure design samples. The design variation amounted to 100 samples generated using Latin Hypercube Sampling (LHS) to distribute the variables. Finally, the Machine Learning method predicted the design that yielded maximum SEA. The optimization process through Machine Learning consisted of two processes: model approximation using an Artificial Neural Network (ANN) and variable optimization using a Nondominated Sorting Genetic Algorithm-II (NSGA-II). The optimization demonstrated that the maximum SEA resulted from Star-shaped auxetic cells and aluminum material with a thickness of 2.95 mm. This design yielded 1220% higher SEA compared to the baseline model. A numerical simulation was also carried out to validate the result. The prediction error amounted to 6.7%, meaning that the approximation model can successfully predict the most optimum design. After the complete battery system configuration simulation, the design could also prevent excessive battery deformation. Therefore, the optimized structure can protect the battery from failure.

Suggested Citation

  • Farras Ezra Carakapurwa & Sigit Puji Santosa, 2022. "Design Optimization of Auxetic Structure for Crashworthy Pouch Battery Protection Using Machine Learning Method," Energies, MDPI, vol. 15(22), pages 1-26, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8404-:d:968766
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    References listed on IDEAS

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    1. Mohammed Mahrach & Gara Miranda & Coromoto León & Eduardo Segredo, 2020. "Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem," Mathematics, MDPI, vol. 8(11), pages 1-23, November.
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    Cited by:

    1. Liviu I. Scurtu & Ioan Szabo & Marius Gheres, 2023. "Numerical Analysis of Crashworthiness on Electric Vehicle’s Battery Case with Auxetic Structure," Energies, MDPI, vol. 16(15), pages 1-18, August.

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