INTELLIGENT FAULT DETECTION AND ISOLATION FOR PROTON EXCHANGE MEMBRANE FUEL CELL SYSTEMS

This work presents a new approach for detecting and isolating faults in nonlinear processes using independent neural network models. In this approach, an independent neural network is used to model the proton exchange membrane fuel cell nonlinear systems using a multi-input multi-output structure. This research proposed the use of radial basis function network and multilayer perceptron network to perform fault detection. After training, the neural network models can give accurate prediction of the system outputs, based on the system inputs. Using the residual generation concept developed in the model-based diagnosis, the difference between the actual and estimated outputs are used as residuals to detect faults. When the magnitude of these residuals exceed a predefined threshold, it is likely that the system is faulty. In order to isolate faults in the system, a second neural network is used to examine features in the residual. A specific feature would correspond to a specific fault. Based on features extracted and classification principles, the second neural network can isolate faults reliably and correctly. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations

Fault tolerant control of a PEM fuel cell using qLPV virtual actuators

This paper proposes a fault tolerant control (FTC) strategy based on the use of quasi-linear parameter varying (qLPV) virtual actuators approach for proton exchange membrane (PEM) fuel cells. The overall solution relies on adding a virtual actuator in the control loop to hide the fault from the controller point of view, allowing it to see the same plant as before the fault, in this way keeping the stability and some desired performances. The proposed methodology is based on the use of a reference model, where the resulting nonlinear error model is brought to a qLPV form that is used for control design by means of linear matrix inequalities (LMI)-based techniques. The resulting closed-loop error system is stable with poles placed in some desired region of the complex plane. Simulation results are used to show the effectiveness of the proposed approach.Peer ReviewedPostprint (published version

PROGNOSTIC AND HEALTH-MANAGEMENT ORIENTED FUEL CELL MODELING AND ON-LINE SUPERVISORY SYSTEM DEVELOPMENT

Of the fuel cells being studied, the proton exchange membrane fuel cell (PEMFC) is viewed as the most promising for transportation. Yet until today, the commercialization of the PEMFC has not been widespread in spite of its large expectation. Poor long term performances or durability, and high production and maintenance costs are the main reasons. For the final commercialization of fuel cells in the transportation field, durability issues must be addressed, while costs should be further brought down. At the same time, health-monitoring and prognosis techniques are of great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems. This dissertation presents a comprehensive on-line supervisory system to address the important issues related to the PEMFC durability, including: 1) diagnosis of critical operating conditions, 2) optimization of the operating conditions, and 3) health monitoring (or damage tracking) and remaining useful life (RUL) prediction. In order to design and implement this supervisory system, a comprehensive fuel cell model is developed that integrates a control/diagnostic oriented dynamic fuel cell model and a prognostic oriented fuel cell degradation model, due to a lack of such models in the existing literature. To address the first issue, a model-based on-line diagnostics system is developed for fuel cell flooding and drying diagnosis, thanks to the incorporation of the diagnostic feature in the dynamic fuel cell model. The channel flooding diagnostic problem is decoupled from the gas diffusion layer (GDL) flooding and membrane drying diagnostic problem. Simultaneous state and parameter estimation problems are formulated for both cases. Dual extended Kalman filter (EKF) and dual unscented Kalman filter (UKF) techniques are applied respectively to solve the problems. The second issue is addressed by a diagnostic based control design for the air supply of the fuel cell system. The design concept allows selection of the most suitable controller in a controller bank that delivers the best performance under specific operating conditions and that mitigates the faulty condition based on the feedback of the diagnosis results. The control problem is reformulated as an H-infinity robust control problem, the objective of which is to minimize the difference between the desired and actual excess O2 ratio, thus preventing and minimizing oxidant starvation at the cathode. Finally, an UKF-based health-monitoring and prognostic scheme is proposed and applied to the damage tracking and RUL prediction for the fuel cell. The developed aging model is employed as the kernel for this scheme, which utilizes the fuel cell output voltage as the only feature for the prognostic and health management task

Technology 2001: The Second National Technology Transfer Conference and Exposition, volume 2

Proceedings of the workshop are presented. The mission of the conference was to transfer advanced technologies developed by the Federal government, its contractors, and other high-tech organizations to U.S. industries for their use in developing new or improved products and processes. Volume two presents papers on the following topics: materials science, robotics, test and measurement, advanced manufacturing, artificial intelligence, biotechnology, electronics, and software engineering