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September 10, 2025Journal of Autonomous Vehicles and Systems

Optimizing charging control for fast and efficient electric vehicle charging

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Authors

HLHan LiHLHaijuan LiuYZYanxue Zhang

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Overview

Machine-learning model predicts optimal charging control for electric vehicles, enhancing grid stability and energy utilization.

Key Points

  • The Dual-Level Voting Boost algorithm achieved 95% accuracy in predicting optimized charging sessions.
  • Using various features like battery levels and ambient temperature, the model effectively determines charging outcomes.
  • Developed through a two-stage ensemble learning approach, the DLVB outperformed traditional single-model methods.
  • This model highlights the importance of effective charging control for electric vehicles in sustaining grid stability.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68c1a8fe54b1d3bfb60e1bc0https://doi.org/10.1115/1.4069313
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