Optimizing electric vehicle driving range prediction using deep learning: A deep neural network (DNN) approach

The rapid growth in the popularity of Electric Vehicles (EVs) requires accurate driving range predictions to minimize range anxiety and optimize trip planning, especially in real-world driving conditions where diverse factors affect range. This study addresses the challenges of EV range prediction by presenting a novel deep learning technique that uses a Deep Neural Network (DNN) model optimized with the RMSProp optimizer. This approach leverages a unique real-world dataset that reflects varied driving environments, leading to superior performance. The model achieves an R2 score of 0.99, a Mean Absolute Percentage Error (MAPE) of 2.01%, a Mean Absolute Error (MAE) of 6.81 km, and a Root Mean Squared Error (RMSE) of 9.32 km. These results significantly outperform conventional machine learning techniques like support vector machines and linear regression, demonstrating the practicality and reliability of the proposed model for reducing EV range anxiety and improving trip planning in real-world scenarios.