Fault Detection Based on Super-Twisting Sliding Mode for PEM Fuel Cells

A fuel cell is an energy converter which converts the chemical energy to the direct current electricity through an electrochemical reaction. Polymer electrolyte membrane (PEM) fuel cell is one of the types of fuel cells which has various industrial and military applications. The PEM fuel cells cannot work safely without any fault detection and identification approaches and are exposed to mechanical and system faults which may result in disastrous events. Therefore, we design robust fault detection and identification approach based on the super-twisting second order sliding mode method. Sliding mode control has been an active area of research for many decades due its (at least theoretical) invariance to a class of uncertainty known as matched uncertainty. More recently these ideas have been exploited extensively for the development of robust observers and have found applications in the area of fault detection and fault tolerant control. Super twisting version of sliding mode family has smoother signal in comparison with conventional first order sliding mode.A fuel cell is an energy converter which converts the chemical energy to the direct current electricity through an electrochemical reaction. Polymer electrolyte membrane (PEM) fuel cell is one of the types of fuel cells which has various industrial and military applications. One of the important issues in the PEM fuel cells is the oxygen starvation phenomenon under fast load changes. Preventing from the oxygen starvation phenomenon is a challenging duty which has two aspects: firstly, measuring the oxygen excess ratio is not easy and secondly the fuel cell is affected by different faults; for instance, air leak in the supply manifold and increase in the friction of the compressor motor. Therefore, these faults should be identified and compensated. Indeed, the PEM fuel cells cannot work safely without any fault detection and identification approaches and are exposed to mechanical and system faults which may result in disastrous events. However, most of the recent works in the literature consider the simplified nonlinear model or linearized model and only address the air management problem in the PEM fuel cells (see for instance (۱), (۲)). Also, the recent works in the literature only propose the fault detection methods in the PEM fuel cells and ignore the problem of fault reconstruction and identification (see for instance (۳)). In this paper, based on the super-twisting sliding mode method we design a robust fault detection and identification approach. Indeed, in this approach the super-twisting sliding mode method is used for the system state estimation and rebuilding the fault signal We consider the nonlinear dynamical model of a PEM fuel cell including air supply subsystem, dynamics of oxygen, nitrogen and vapor in cathode and dynamics of hydrogen and vapor in anode. Figure ۱ shows this fuel cell system. We use the super-twisting sliding mode method to design a nonlinear observer. Then, the fault signal is rebuilt from the difference between the system states and their estimations. Hence, robust fault detection and identification approach is designed based on the super-twisting sliding mode methodA nonlinear dynamical model for PEM fuel cell has been considered. Then, based on the super-twisting sliding mode method a robust fault detection and identification approach has been designed.

کلیدواژه ها:

Polymer electrolyte membrane (PEM) fuel cell system ، Fault detection and identification ، Robust Observer ، super-twisting sliding mode