Convex Optimization of Speed and Energy Management System for Fuel Cell Hybrid Trains

TL;DR

This paper presents a convex optimization approach to simultaneously optimize speed and energy management in hydrogen hybrid trains, significantly reducing fuel consumption through a holistic, single-optimization framework.

Contribution

It introduces a novel convex optimization method that optimizes both speed and energy management trajectories concurrently for fuel cell hybrid trains.

Findings

Concurrent optimization reduces fuel consumption more than sequential methods.

The barrier method efficiently computes optimal trajectories within seconds.

Holistic optimization leverages powertrain knowledge for better performance.

Abstract

We look into minimizing the hydrogen fuel consumption of hydrogen hybrid trains by optimizing their operation. The powertrain considered is a fuel cell charge-sustaining hybrid. Convex optimization is utilized to compute optimal speed and energy management trajectories. The barrier method is used to solve the optimization problems quickly on the order of tens of seconds for the entire journey. Simulations show a considerable reduction in fuel consumption when both trajectories -- speed and energy management -- are optimized concurrently within a single optimization problem in comparison to being optimized separately in a sequential manner -- optimizing energy management after optimizing speed. It is concluded that the concurrent method greatly benefits from its holistic powertrain knowledge while optimizing all trajectories together within a single optimization problem.