Energy-saving optimization solution for multiple freight trains based on maFOA algorithm

Unlike traditional energy-saving optimization methods for freight trains, which focus solely on reducing the mechanical energy consumption of the trains, the freight train energy-saving optimization approach integrating the traction drive system and AC traction network models concentrates on lowering energy consumption at traction substations. This study began by building models to simulate train dynamics, traction drive systems, and AC traction networks, collectively forming a "train-track-grid" model based on their coupling relationships. Relevant state variables were converted from the spatial domain to the time domain using a linear interpolation method. Additionally, a multi-strategy adaptive fruit fly optimization algorithm for population partitioning (maFOA) was proposed, to address the challenges faced by existing algorithms in solving complex nonlinear “train-track-grid" models, which often result in low convergence accuracy. The effectiveness of the proposed energy-saving optimization strategy for multiple freight trains was verified through experiments. The results exhibit a 49.2% improvement from the strategy in the regenerative braking energy utilization rate, along with a 0.35% reduction in traction network losses.