Abstract
A holistic framework for multi-objective optimization of the traction system configuration of trains with mixed-integer decision variables is presented. Rail vehicles have to be energy-efficient and must be operated on a tight schedule. Furthermore, the number of decision variables to fulfill these objectives is large, and some components (like motors and gears) can only be chosen from a small set of discrete elements. In this work, the overall optimization is achieved by a two level approach: The Pareto front of optimal system configurations is obtained by a multi-objective mixed-integer elitist genetic algorithm (GA) on the upper-level. To capture the influence of a specific system configuration on travel time and energy consumption, a suitable train trajectory optimizer is developed and employed in the lower-level. The train trajectory optimization is solved by sequential quadratic programming (SQP) and considers the power losses of the different components. A case study is presented which highlights the benefits of the holistic multi-objective optimization.
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