STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research

AI-Driven Topology Optimization of Woven Fiber-Reinforced Composite Chassis Structures for Electric Vehicles Under Crash Loading

  1. Sweety Jachak Department of Computer Engineering, Guru Gobind Singh College of Engineering and Research Centre, SPPU, Nashik
  2. Pankaj Vispute Department of Electronics and Telecommunication Engineering, Shatabdi Institute of Engineering and Research, SPPU, Nashik
  3. Sapna Sonar Department of Electrical Engineering, Shatabdi Institute of Engineering and Research, SPPU, Nashik
  4. Swapnil Ratnakar Department of Mechanical Engineering, Shatabdi Institute of Engineering and Research, Nashik
  5. Piyush Bhamare Department of Mechanical Engineering, Shatabdi Institute of Engineering and Research, Nashik
  6. Gokul Mahajan Department of Mechanical Engineering, Shatabdi Institute of Engineering and Research, Nashik
  7. Krupal Pawar Department of Mechanical Engineering, Rajiv Gandhi College of Engineering, SPPU, Ahilyanagar

Abstract

The structural design of an electric vehicle (EV) chassis represents a unique engineering challenge to achieve minimal weight while meeting occupants' safety requirements during high-energy crash conditions without compromise to the battery housing's integrity or the geometrical constraints of the electric powertrain package. In this paper, a single framework is proposed to integrate physics-based artificial intelligence (AI) surrogate models using PINNs, CNN-accelerated topology optimization, and FEA to design woven fiber-reinforced polymer (WFRP) composite chassis components that are optimized specifically for frontal, side, and rear crash loading conditions. Due to the complex interlocking geometry of the woven reinforcement and the manufacturing anisotropy of the fabric, the complex geometry and the anisotropy of the fabric are directly modeled in the constitutive relationship of the material and the AI surrogate. Starting with a three-dimensional design space discretized into 1.2 million hexahedral elements, the proposed pipeline significantly reduced the optimization wall clock time by 78% relative to the SIMP method while providing a 14.3% reduction in mass and an 11.7% increase in the specific energy absorption (SEA) of the WFRP chassis component relative to a similar mass steel reference structure. A progressive damage model was used to model the inter-fiber crack growth and the matrix crack growth under dynamic crushing of the fiber tow and the matrix. The progressive damage model incorporates the Hashin failure criterion and a modified Puck inter-fiber fracture model. The developed framework was verified by comparing the predicted response to drop tower test data and sled test data from a purpose-built EV prototype chassis, indicating that the prediction error of the peak crush force was less than 7.2%, and the prediction error of the SEA was less than 5.9%. These results indicate that AI-based composite topology optimization is ready to be deployed in industry as a tool for designing EV chassis components.

Keywords

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