2025

Journal

Computers and Electrical Engineering, 123, 110185.

To overcome Climate Change, countries are turning to greener transportation systems. Therefore, the use of Electric Vehicles (EVs) is leveraging substantially since they present multiple advantages, like reducing hazardous emissions. Recently, the demand for EVs has increased, which means that more charging stations need to be available. By the year 2030, 15 million EVs will be accessible, and since the number of charging stations is limited, the charging needs should be defined for better management of the charging infrastructure. In this research, we aim to tackle this problem by efficiently predicting the energy consumption of EVs. We proposed a Genetic Algorithm (GA) based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM), which is an optimized LSTM model that incorporates a GA for Hyperparameter Tuning. After experimenting our methodology and performing a comparative analysis with previous studies from the literature, the obtained results showed the efficiency of our novel model, with Mean Squared Error (MSE) equals to 0.000112 and a Determination Coefficient (R) equals to 0.96470. It outperformed other models of the literature for predicting energy use based on real-world data collected from the campus of Georgia Tech in Atlanta, USA.

@article{jlifi2025genetic,
  title={A genetic algorithm based three hyperparameter optimization of deep long short term memory (GA3P-DLSTM) for predicting electric vehicles energy consumption},
  author={Jlifi, Boutheina and Ferjani, Syrine and Duvallet, Claude},
  journal={Computers and Electrical Engineering},
  volume={123},
  pages={110185},
  year={2025},
  publisher={Elsevier}
}