Model based power and energy management system for PEM fuel cell/ LI-Ion battery driven propulsion system

Abstract

Unmanned aerial vehicle (UAV) electric propulsion is a modern aviation concept that has long endurance capacity compared to the conventional IC engines. The polymer exchange membrane (PEM) fuel cell and Li-Ion battery hybrid power plant shows a great potential to replace the IC engines from UAVs due to high energy and power densities of the power sources. The power and energy management is an important consideration in PEM fuel cell/ Li-Ion battery hybrid power system to optimize the energy sharing between two power sources. The energy management system (EMS) has long term objectives to maximize the FC net power output by minimizing the oxygen concentration voltage losses. The EMS receives the feedbacks from the battery, the load power and the FC system control parameters. The decisions taken by the EMS are passed to the power management system (PMS) which makes the short term policies to control the power electronic interface (PEI). The PEI is consisted with the bidirectional controller for the battery and the DC boost converter for the PEMFC system. The PMS has rule base system to decide the operating power of the PEMFC system. In addition to that, the PMS decide the bidirectional converter current flow direction to charge or discharge the battery. The EMS system controls the FC system inlet air flow rate by changing the compressor motor voltage to the optimum values. The Adaptive Neuro Fuzzy Inference System (ANFIS) based online learning and adaptive control algorithms are used to train the existing compressor voltage into the optimum values which are obtained from the reference model. In this power and energy management system, the FC system is operated at optimum power region and the battery supplies the transient power to the propulsion system. (6 pages)