Paper Title
Sequential Deep Learning for Electric Vehicle Energy Consumption Forecasting

Abstract
Electric vehicles (EVs) play an important role in reducing air pollution and making transportation more sustainable. As more people use cars, emissions from fuel-powered vehicles are increasing, which is a big concern for the environment. EVs help solve this problem by providing a cleaner alternative. However, for EVs to be widely used, we need accurate energy consumption models that can predict how much battery power a car will use on a trip. Many factors affect energy use, such as the way a person drives, road conditions, traffic, and weather. If these factors are well understood, EVs can be made more efficient, helping to reduce driver worries about running out of charge. Accurate predictions also make it easier to plan trips and improve charging station networks. Advanced technologies like artificial intelligence and deep learning can help improve these energy models, making EVs more reliable and practical for everyday use. This study aims to employ advanced machine-learning models to predict energy consumption to tackle the challenge of electric vehicles' energy consumption prediction.