2024/10/23 by Do Young Kim, Kim, Do Young, Kyunghyun Kim +7
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #Distributed and Parallel Computing Systems #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2410.17712
openalex publication_date 2024/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Solar vehicles, which simultaneously produce and consume energy, require meticulous energy management. However, potential users often feel uncertain about their operation compared to conventional vehicles. This study presents a simulator designed to help users understand long-distance travel in solar vehicles and recognize the importance of proper energy management. By utilizing Google Maps data and weather information, the simulator replicates real-world driving conditions and provides a dashboard displaying vehicle status, updated hourly based on user-inputted speed. Users can explore various speed policy scenarios and receive recommendations for optimal driving strategies. The simulator's effectiveness was validated using the route of the World Solar Challenge (WSC). This research enables users to monitor energy dynamics before a journey, enhancing their understanding of energy management and informing appropriate speed decisions.