Hybrid railway vehicle trajectory optimisation using a non-convex function and evolutionary hybrid forecast algorithm

This paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a non-linear programming solver with the highly efficient Mayfly Algorithm to address a non-convex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.

Automated summary

This paper introduces a novel optimisation algorithm that combines a non-linear programming solver and the Mayfly Algorithm to address a non-convex optimisation problem in hybrid railway vehicles. The algorithm generates efficient trajectories to ensure effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm helps lower maintenance costs, reduce downtime, and extend operational life of these sources. It considers various operational parameters and leverages the hybrid powertrain capabilities of the vehicles to optimise energy consumption throughout the journey.