Abstract
Abstract
Railway systems stand out as highly efficient modes of transportation compared to others, leading to a rising demand for the sake of research and development aimed at reducing their energy consumption. This pursuit not only enhances sustainability but also addresses the pressing issue of climate change. A multitude of studies delve into modeling, analyzing, and optimizing energy usage within railway systems, showcasing a diverse array of methodologies and techniques for formulating, and solving optimization problems. This review paper undertakes a comparative examination of approximately 36 relevant studies focusing on railway energy consumption encompassing both traction and auxiliary energy. The research emphasizes various modeling techniques employed in simulating train movement and energy consumption; alongside different optimization methods focused at improving operational efficiency on railway tracks. It meticulously scrutinizes the most prevalent optimization methods, techniques and variables are utilized. Through an extensive review of literature, it becomes apparent that deterministic approaches, particularly based on the Davis equations, dominate the modeling landscape, accounting for over 80% of the approaches. However, when it comes to optimization, meta-heuristic approaches take precedence, with Genetic Algorithms being a prominent choice. These findings underscore the significance of meta-heuristic approaches, crucial for enhancing both human activities and mitigating energy consumption, especially in a heavy energy-consuming sector like railway transportation.