Multi-objective operation control of rail vehicles

Train operation energy consumption occupies a large proportion of whole rail transport resource consumption. Aiming at improving energy utilization, current research is mainly about saving energy while security and time are limiting conditions. However, actual train operation is a complex process which has strict requirements on safety, energy consumption, precise parking and some other factors. This paper summarizes train control optimization research, analyzes train running characteristics and influential factors, and introduces train traction computing method. Multi-objective Particle Swarm Optimization with inertia weight algorithm is applied to study train operation optimization. With energy-saving, punctuality and precise parking being optimization goals, train operation optimization models and fitness evaluation functions are established. Through optimization, optimal operating condition sequence and corresponding condition conversion points are obtained. Effectiveness of the proposed algorithm is verified by offline simulations, and the results present good optimization performance.