Optimizing Electric Vehicle Energy Management Systems with a Hybrid LSTM-CNN Architecture

Abstract

This study explores the integration of Electric Vehicles (EVs) into the global transportation network, emphasizing the role of advanced Energy Management Systems (EMS) in enhancing the efficiency, reliability, and sustainability of EVs. Despite significant strides in predictive modeling for energy consumption, current methodologies face challenges such as handling high-dimensional data and adapting to dynamic urban traffic conditions. To address these limitations, this research introduces a novel hybrid Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architecture designed to optimize energy consumption predictions by integrating both temporal and spatial data analyses. The proposed model demonstrates predictive superiority over existing models, validated through extensive experimentation with the comprehensive EV Energy Consumption and Speed Profiles Dataset (EVECS). This paper not only contributes to advancing predictive modeling capabilities within smart transportation systems but also lays the groundwork for future innovations in the sustainable integration of EVs into smart cities.