Abstract
Electrification of locomotive with hybridized fuel-cell, battery and supercapacitor has drawn much attention from both the academia and industry. Unlike traditional powertrain, hybrid powertrain consists of multiple power sources with a complex drivetrain structure, various efficiency performance, and different dynamics. Therefore, it is necessary to develop a power management strategy to make sure each power source operates under a quasi-optimal condition and maximize the overall powertrain efficiency. This paper presents the development of a power management framework for a novel hybrid locomotive consisting of PEM fuel cell, battery, and supercapacitor. Both the equivalent consumption management strategy (ECMS) and the stochastic dynamic programming (SDP) are applied to solve for the optimal power split strategy. The resulted power management strategy is presented in the form of policy maps, which makes it convenient for real-time in-vehicle implementations. Simulation results indicate that the SDP demonstrates advantages over the ECMS in terms of equivalent hydrogen consumption over typical locomotive driving cycles.
Performance enhancement and power management strategy of an autonomous hybrid fuel cell/wind power system based on adaptive neuro fuzzy inference system
