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The use of electric vehicles has recently increased, along with public awareness of the importance of protecting against climate change. Electric vehicles have limited battery capacity, so reducing the frame weight can save energy consumption, allowing the vehicle to operate longer. Topology optimization is one way to reduce the weight of the electric vehicle frame while considering the applied minimum stress to ensure safety during riding. The topology optimization process is repeated by varying input parameters (i.e., percent to retain and retained threshold) and calculating minimum weight and minimum stress as responses using ANSYS 2021 R2. The Backpropagation Neural Network (BPNN) and Genetic Algorithm (GA) are then used to find an optimum solution using MATLAB R2022B. The optimum value is obtained by selecting the percent to retain of 45% and the retained threshold of 27%. The selected optimization parameters are then used for validation as input for the topology optimization method. As a result, the mass of the electric bike frame can be reduced to 39.26%, and the stress increased to 12.512%.

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Optimization of Electric-Bike’s Frame Using Topology Optimization, Back Propagation Neural Network and Genetic Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • M. K. Effendi,
  • A. S. Pramono,
  • C. R. Kurniawan,
  • D. M. Fellicia,
  • F. A. Pamuji,
  • D. Harnany,
  • B. Sudarmanta

摘要

The use of electric vehicles has recently increased, along with public awareness of the importance of protecting against climate change. Electric vehicles have limited battery capacity, so reducing the frame weight can save energy consumption, allowing the vehicle to operate longer. Topology optimization is one way to reduce the weight of the electric vehicle frame while considering the applied minimum stress to ensure safety during riding. The topology optimization process is repeated by varying input parameters (i.e., percent to retain and retained threshold) and calculating minimum weight and minimum stress as responses using ANSYS 2021 R2. The Backpropagation Neural Network (BPNN) and Genetic Algorithm (GA) are then used to find an optimum solution using MATLAB R2022B. The optimum value is obtained by selecting the percent to retain of 45% and the retained threshold of 27%. The selected optimization parameters are then used for validation as input for the topology optimization method. As a result, the mass of the electric bike frame can be reduced to 39.26%, and the stress increased to 12.512%.