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<p>The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and velocity using a synthetically generated dataset of 2,000 samples designed to reflect real-world driving scenarios. Four models—Random Forest (RF), Extra Trees (ET), Linear Regression (LR), and Long Short-Term Memory (LSTM)—were evaluated individually and in ensemble combinations. To ensure statistical reliability, all models were trained and tested over ten independent runs with randomized data partitions, and the results were reported as average performance with standard deviations. The ensembles consistently outperformed individual models, with the full ensemble (RF + ET + LSTM + LR) achieving the most robust performance across all metrics (MAE, MSE, and R²). Furthermore, a real-time web application was developed using the trained models to dynamically estimate driving parameters. The findings highlight the potential of integrating AI-driven predictive modelling into EV systems to support efficient driving behaviour and energy management.</p>

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Smart Strategies for Improving Electric Vehicle Battery Performance and Efficiency

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Swathi Tangi,
  • Ayush Vatsa,
  • Akshat Opam,
  • Praveen Kumar Bonthagorla,
  • D. N. Gaonkar

摘要

The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and velocity using a synthetically generated dataset of 2,000 samples designed to reflect real-world driving scenarios. Four models—Random Forest (RF), Extra Trees (ET), Linear Regression (LR), and Long Short-Term Memory (LSTM)—were evaluated individually and in ensemble combinations. To ensure statistical reliability, all models were trained and tested over ten independent runs with randomized data partitions, and the results were reported as average performance with standard deviations. The ensembles consistently outperformed individual models, with the full ensemble (RF + ET + LSTM + LR) achieving the most robust performance across all metrics (MAE, MSE, and R²). Furthermore, a real-time web application was developed using the trained models to dynamically estimate driving parameters. The findings highlight the potential of integrating AI-driven predictive modelling into EV systems to support efficient driving behaviour and energy management.