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Generative Adversarial Network for Synthesizing Multivariate Time-Series Data in Electric Vehicle Driving Scenarios.

Shyr-Long Jeng1

  • 1Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan.

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Summary

A novel generative model synthesizes realistic electric vehicle (EV) driving data, accurately predicting key operational parameters like battery state of charge (SOC). This advancement aids EV technology and battery management systems.

Keywords:
SOC estimationconditional generative adversarial networksprincipal component analysis (PCA)time-series synthesis

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Automotive Engineering

Background:

  • Accurate electric vehicle (EV) operational data is crucial for development and management.
  • Existing datasets may lack the diversity or detail needed for advanced simulations.
  • Synthesizing realistic driving data presents a significant challenge.

Purpose of the Study:

  • To introduce a time-series point-to-point generative adversarial network (TS-p2pGAN) for realistic EV driving data synthesis.
  • To accurately generate multivariate time-series data for critical EV operational parameters.
  • To validate the model's performance on real-world driving data.

Main Methods:

  • Development and application of a TS-p2pGAN model.
  • Generation of battery state of charge (SOC), voltage, mechanical acceleration, and vehicle torque data.
  • Evaluation using quantitative metrics (RMSE, MAE, DTW) and qualitative analysis (PCA, t-SNE).

Main Results:

  • The TS-p2pGAN model achieved high accuracy in generating EV driving data, with key metrics below 3% (RMSE), 1.5% (MAE), and 2.0% (DTW).
  • Exceptional SOC estimation accuracy was observed, even in complex driving conditions and varied initial SOC levels.
  • Qualitative analysis confirmed the preservation of data distributions and temporal dynamics.

Conclusions:

  • The TS-p2pGAN framework effectively synthesizes realistic EV driving data, offering a valuable tool for research and development.
  • This data augmentation approach has significant implications for advancing EV technology, battery management, and autonomous driving systems.
  • The model's ability to capture complex operational dynamics enhances its utility for digital energy management and comfort system development.