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This paper introduces an intelligent control strategy based on neural networks to optimize energy consumption in autonomous vehicles. The proposed approach leverages a recurrent neural network (RNNRecurrent Neural Network (RNN)) to forecast power demand in real time, considering vehicle dynamics and environmental conditions, while an ensemble learning model enhances adaptive control decisions. Unlike traditional rule-based energy managementEnergy management systems, this hybrid architecture dynamically adjusts energy usage from multiple sources—including battery systems, regenerative braking, and auxiliary inputs—based on real-time driving contexts such as speed variations, traffic patterns, and terrain changes. Designed for deployment on embedded automotive hardware, the system enables localized, autonomous decision-making with minimal computational overhead. Experimental evaluations on realistic driving datasets demonstrate superior performance in terms of energy efficiency, responsiveness to uncertainty, and overall system stability, validating the potential of neural network-based control for intelligent energy managementEnergy management in next-generation autonomous vehicles.

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Harnessing Neural Networks for Controlling Energy Consumption for Autonomous Vehicles

  • 会议论文

  • 发表日期：

    2026年

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  • Souad Touairi,
  • Nidal Ghalim,
  • Hanaa Ouaomar,
  • Mustapha Mabrouki,
  • Nourreeddine Kouider

摘要

This paper introduces an intelligent control strategy based on neural networks to optimize energy consumption in autonomous vehicles. The proposed approach leverages a recurrent neural network (RNNRecurrent Neural Network (RNN)) to forecast power demand in real time, considering vehicle dynamics and environmental conditions, while an ensemble learning model enhances adaptive control decisions. Unlike traditional rule-based energy managementEnergy management systems, this hybrid architecture dynamically adjusts energy usage from multiple sources—including battery systems, regenerative braking, and auxiliary inputs—based on real-time driving contexts such as speed variations, traffic patterns, and terrain changes. Designed for deployment on embedded automotive hardware, the system enables localized, autonomous decision-making with minimal computational overhead. Experimental evaluations on realistic driving datasets demonstrate superior performance in terms of energy efficiency, responsiveness to uncertainty, and overall system stability, validating the potential of neural network-based control for intelligent energy managementEnergy management in next-generation autonomous vehicles.