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Estimating Battery Life in Electric Vehicles using Deeper Long Short-Term Memory (DLSTM) Algorithm

Author

Listed:
  • Le Viet Bach

    (VNU University of Science, Vietnam)

  • Vo Thanh Ha

    (University of Transport and Communications, Vietnam)

Abstract

The paper estimates electric vehicle battery life using a deep, long-term memory algorithm (DLSTM). This algorithm employs a Forget Gate with a sigmoid function to retain or discard information from previous states. The Input Gate, also using a sigmoid function, determines new information to add, while a tanh function creates a new vector for updating the cell state. The Cell State Update combines inputs from the forget and input gates. The Output Gate uses a sigmoid function to select which part of the cell state to output. This AI algorithm analyses voltage, current, and temperature during charging to predict the lithium-ion battery’s state.

Suggested Citation

  • Le Viet Bach & Vo Thanh Ha, 2024. "Estimating Battery Life in Electric Vehicles using Deeper Long Short-Term Memory (DLSTM) Algorithm," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 8(6), pages 21-25, October.
    • Handle: RePEc:epw:ejece0:v:8:y:2024:i:6:id:19667
    DOI: 10.24018/ejece.2024.8.6.667
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