A Bi-Level Optimization of Speed and Energy Management for Diesel-Electric Hybrid Train

Abstract

Diesel-electric hybrid trains (DEHT) equipped with battery systems can effectively reduce fuel consumption and greenhouse gas emissions. For chasing the best fuel consumption efficiency, the coupled optimization problem of driving strategy and energy management for DEHT is addressed in this paper. We propose a bi-level optimization method that jointly designs driving strategy and energy management for DEHT. The optimal driving strategy is obtained by modeling a particle swarm optimization problem in fuel consumption reduction at the top level. The power allocation for the diesel generator set and the battery is described as a convex optimization problem and solved at the bottom level. The proposed method can converge to an optimal value after cyclic iterations. In each iteration, the top-level outputs the current optimal driving strategy to the bottom problem. Based on this current optimal driving strategy, the bottom level calculates the current best fuel consumption and energy management, then feeds back to the top level to start a new iteration. To verify our introduced method, the proposed optimization is compared with the common problems, such as single energy management optimization, single-speed optimization, and sequence optimization. Results show that the maximum and minimum fuel consumption reduction by the introduced optimization method is 20.0% and 2.7%, respectively.