Dynamic Response of an Indirect-Methanol Fuel Cell Vehicle

Hauer, Karl-Heinz; Friedmann, D. J.; Moore, Robert M.; Ramaswamy, Sitaram; Eggert, Anthony; Badrinarayanan, P.

doi:10.4271/2000-01-0370

Cited by 15 publications

(6 citation statements)

References 1 publication

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“…Few papers address the effects of transient variations 1Support is provided by the U.S. Army Center of Excellence for Automotive Research, Contract DAAE07-98- [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] in the fuel cell system performance. Excellent examples are the dominant but slow temperature effects on the stack efficiency [8,9], and the effects of reformed hydrogen/-/2 feed rates in the stack response [10,11]. Last but not least, the FC dynamic behavior due to changes in reactant flow is modeled in [12] and presented in [13].…”

Section: Introductionmentioning

confidence: 99%

Modeling and control for PEM fuel cell stack system

Pukrushpan

Stefanopoulou

Peng

2002

Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)

230

140

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A nonlinear fuel cell system dynamic model that is suitable for control study is presented. The transient phenomena captured in the model include the flow characteristics and inertia dynamics of the compressor, the manifold filling dynamics, and consequently, the reactant partial pressures. Characterization of the Fuel Cell polarization curves based on time varying current, partial oxygen and hydrogen pressures, temperature, membrane hydration allows analysis and simulation of the transient fuel cell power generation. An observer based feedback and feedforward controller that manages the tradeoff between reduction of parasitic losses and fast fuel cell net power response during rapid current (load) demands is designed.

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Section: Introductionmentioning

confidence: 99%

Modeling and control for PEM fuel cell stack system

Pukrushpan

Stefanopoulou

Peng

2002

Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)

230

140

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“…By including only temperature dynamics, the system transient behavior can be clearly observed during the warm-up period as shown in [20]. Hauer et al [58] represented the dynamics of the fuel reformer with the dynamics of its temperature rise by using a second-order transfer function with an adjustable time constant. Kim and Kim [66] simplified the system model further by using first-order time delay electrical circuit to represent the fuel reformer and the fuel cell stack voltage.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance of Fuel Cell System for Medium Duty Truck by Cooling System Configuration

Woo¹,

Kim²,

Yu³

2021

KHNES

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Fuel cell systems offer clean and efficient energy production and are currently under intensive development by several manufacturers for both stationary and mobile applications. The viability, efficiency, and robustness of this technology depend on understanding, predicting, and controlling the unique transient behavior of the fuel cell system. In this thesis, we employ phenomenological modeling and multivariable control techniques to provide fast and consistent system dynamic behavior. Moreover, a framework for analyzing and evaluating different control architectures and sensor sets is provided.Two fuel cell related control problems are investigated in this study, namely, the control of the cathode oxygen supply for a high-pressure direct hydrogen Fuel Cell System (FCS) and control of the anode hydrogen supply from a natural gas Fuel Processor System (FPS). System dynamic analysis and control design is carried out using model-based linear control approaches. A system level dynamic model suitable for each control problem is developed from physics-based component models. The transient behavior captured in the model includes flow characteristics, inertia dynamics, lumpedvolume manifold filling dynamics, time evolving spatially-homogeneous reactant pressure or mole fraction, membrane humidity, and the Catalytic Partial Oxidation (CPOX) reactor temperature.The goal of the FCS control problem is to effectively regulate the oxygen concentration in the cathode by quickly and accurately replenishing oxygen depleted during power generation. The features and limitations of different control configurations and the effect of various measurement on the control performance are examined. For example, an observability analysis suggests using the stack voltage measurement as feedback to the observer-based controller to improve the closed loop performance. The objective of the FPS control system is to regulate both the CPOX temperature and anode hydrogen concentration. Linear multivariable system analysis is used to identify the limitation of a decentralized controller and to design a model-based multivariable controller with significantly improved performance in CPOX temperature regulation. Further analysis unveils the critical controller cross-coupling term that contributes to the superior performance of the multivariable controller.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Membrane Water Content Characteristics through Dynamic Nonlinear Model

LEE¹,

KIM²,

Yu³

2021

KHNES

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for their help and for sharing with me their insightful knowledge of fuel processor systems.I am grateful to Scott Staley, Doug Bell, Woong-Chul Yang, and James Adams at the Ford Motor Company for their helpful suggestions and for providing me with valuable data on the fuel cell. I also owe a note of thanks to Larry Mianzo and Shankar Raman for allowing me to earn valuable research and work experience during my internship at Visteon Corporation.At the University of Michigan, I have worked alongside many talented students. My thanks go to all of them for their kindness and friendship, but particularly to

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Dynamic Response of an Indirect-Methanol Fuel Cell Vehicle

Cited by 15 publications

References 1 publication

Modeling and control for PEM fuel cell stack system

Modeling and control for PEM fuel cell stack system

Performance of Fuel Cell System for Medium Duty Truck by Cooling System Configuration

Prediction of Membrane Water Content Characteristics through Dynamic Nonlinear Model

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