A nonlinear sliding mode observer for the estimation of temperature distribution in a planar solid oxide fuel cell

Cheng, Huan; Li, Xi; Jiang, Jianhua; Deng, Zongquan; Yang, Jie; Li, Jian

doi:10.1016/j.ijhydene.2014.10.117

Cited by 45 publications

(14 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final form of the discretetime fundamental model is as follows. From the discrete transfer functions (15), (16), (17), (18), and (19), direct dependence of current values of process variables (i.e., for the current sampling instant ) as functions of corresponding variables in the previous instants is found. From (15), one has…”

Section: Model Discretisationmentioning

confidence: 99%

See 1 more Smart Citation

Towards Reduced‐Order Models of Solid Oxide Fuel Cell

Ławryńczuk

2018

Complexity

View full text Add to dashboard Cite

The objective of this work is to find precise reduced-order discrete-time models of a solid oxide fuel cell, which is a multiple-input multiple-output dynamic process. At first, the full-order discrete-time model is found from the continuous-time first-principle description. Next, the discrete-time submodels of hydrogen, oxygen, and water pressures (intermediate variables) are reduced. Two model reduction methods based on observability and controllability Grammians are compared: the state truncation method and reduction by residualisation. In all comparisons, the second method gives better results in terms of dynamic and steady-state errors as well as Nyquist plots. Next, the influence of the order of the pressure models on the errors of the process outputs (the voltage and the pressure difference) is studied. It is found that the number of pressure model parameters may be reduced from 25 to 19 without any deterioration of model accuracy. Two suboptimal reduced models are also discussed with only 14 and 11 pressure parameters, which give dynamic trajectories and steady-state characteristics that are very similar to those obtained from the full-order structure.

show abstract

Section: Model Discretisationmentioning

confidence: 99%

“…Different variants of nonlinear MPC algorithms for the SOFC are discussed in [13][14][15][16] (different model structures and optimisation algorithms are possible). Important applications 2 Complexity of the mathematical model of the SOFC also include process optimisation [17], fault tolerant control [15], and estimation [16,18].…”

Section: Introductionmentioning

confidence: 99%

Towards Reduced‐Order Models of Solid Oxide Fuel Cell

Ławryńczuk

2018

Complexity

View full text Add to dashboard Cite

show abstract

“…Similar with what we did in [27], the finite-volume method is used again to build the model for a cross-flow stack. Along the air and fuel flow direction, the stack is equidistantly divided into mn  nodes as illustrated in Fig.…”

Section: Modelingmentioning

confidence: 99%

“…The cross-flow stack model in this paper is based on physical conservation laws that are essentially the same, with the co-flow stack model built by us in [27]. The difference between the cross-and co-flow stack models is the method of heat transport in their stack solid structures.…”

Section: Modelingmentioning

confidence: 99%

Control-oriented modeling analysis and optimization of planar solid oxide fuel cell system

Cheng

Jing

et al. 2016

International Journal of Hydrogen Energy

Self Cite

View full text Add to dashboard Cite

Because of multi-timescale characteristics and gas transmission delays, the thermal electrical cooperative control of solid oxide fuel cells is a complex and difficult issue. We have used modeling, analysis and optimization of a solid oxide fuel cell system control to guarantee a high efficiency and temperature safety during steady-state and power switch transients. An analysis-based optimization method that applies to discrete optimization problem with constraints is proposed to obtain optimal operating points with a maximum efficiency and to satisfy temperature constraints. Artificial neural-network models that were identified from a validated

show abstract

“…The working environment of the SOFC stack involves high temperature (the temperature can reach 1100 K in general), and the sealing requirement of the stack is very strict . As for the fault detection of the whole SOFC system, it is also required that one know the characteristics of the stack.…”

Section: Introductionmentioning

confidence: 99%

Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Xue

et al. 2018

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault.

show abstract

A nonlinear sliding mode observer for the estimation of temperature distribution in a planar solid oxide fuel cell

Cited by 45 publications

References 17 publications

Towards Reduced‐Order Models of Solid Oxide Fuel Cell

Towards Reduced‐Order Models of Solid Oxide Fuel Cell

Control-oriented modeling analysis and optimization of planar solid oxide fuel cell system

Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Contact Info

Product

Resources

About