Abstract

The surge in greenhouse gas emissions creates a dire need for effective energy management systems (EMSs) that enable the greater utilization of renewable resources to meet the power demand of an electric vehicle. In this study, we develop such an EMS for a solar electric vehicle (SEV). The proposed EMS optimizes the power flows and charging schedule of a SEV so as to minimize the total costs based on the PV generation, varying electricity prices, charging infrastructure, and trip characteristics. A key thrust of the proposed EMS is a deep learning model used to predict the uncertain photovoltaic generation of the vehicle. We illustrate the application of the proposed EMS on two case studies that are distinguished by their driving patterns and charging infrastructures. The results show that in both case studies the proposed EMS resulted in lower costs compared to a simple charging model.