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A conductor needs to be a component of a path that creates a closed loop or full circuit to have a continuous current flowing through it. A current starts to flow if an electric field is created inside an isolated conductor that is not part of a full circuit. The conductor quickly develops a net positive charge at one end and a net negative charge at the other. These charges generate an electric field opposite the direction of the applied electric field, which reduces the current. Eventually,...
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Deep learning based emulator for predicting voltage behaviour in lithium ion batteries.

Kanato Oka1, Naoto Tanibata1, Hayami Takeda1

  • 1Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, Aichi, 466-8555, Japan.

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Summary

This study uses deep learning to create a battery emulator for lithium-ion batteries (LIBs). This machine learning approach accurately predicts battery performance, reducing costs and development time for automotive prototypes.

Keywords:
Battery emulatorCharge–discharge behaviourDeep learningLithium-ion batteries (LIBs)Long short-term memory (LSTM)Time series analysis

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Fabricating large-scale automotive prototype batteries is time-consuming and expensive.
  • Accurate battery performance prediction is crucial for efficient development.
  • Existing emulation methods may lack the precision required for complex battery behaviors.

Purpose of the Study:

  • To develop a data-driven battery emulator for lithium-ion batteries (LIBs) using deep learning.
  • To reduce the economic and temporal costs associated with automotive battery prototyping.
  • To accurately predict the charge-discharge behavior of LIBs under various conditions.

Main Methods:

  • Utilized long short-term memory (LSTM) deep learning models for battery emulation.
  • Trained models on both simulation data (Dualfoil model) and experimental data from LIBs.
  • Employed galvanostatic charge-discharge schedules as arbitrary inputs for prediction.
  • Investigated the impact of state-of-charge descriptors on model performance.

Main Results:

  • Achieved high prediction accuracy for voltage profiles, with R-squared values of 0.98 (simulation) and 0.97 (experimental).
  • Demonstrated that robust model performance can be attained with a minimal dataset (as few as five charge-discharge cycles).
  • Confirmed the significance of state-of-charge descriptors for accurate emulation.

Conclusions:

  • Data-driven emulation using machine learning significantly accelerates battery development.
  • The developed LSTM-based emulator offers a powerful tool for reducing costs and time in large-scale battery production.
  • This approach facilitates faster iteration and optimization in the design of automotive battery systems.