Solid oxide fuel cell stack temperature estimation with data-based modeling – Designed experiments and parameter identification

Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari

doi:10.1016/j.jpowsour.2014.08.130

Cited by 36 publications

(16 citation statements)

References 18 publications

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“…Fig. 6 A process diagram of the 6 kW demonstration SOFC power unit utilized in the THDA algorithm system level evaluation experiments [19]. The same consistent behavior of the THD index can be observed in Figure 8 below, which shows data from a test period when i DC was modified, while keeping the NG flow constant at 11.3 Ndm 3 min -1 .…”

Section: Thda Algorithm Verification In System Environmentsupporting

confidence: 62%

“…The system level experiments included in this work were carried out on a demonstration SOFC power unit hosted by VTT in Finland. The basic setup of the unit and especially the balance‐of‐plant parts of it, are described in and . For this work, the unit was refurbished with two new planar SOFC stacks of 119 cells each, provided by Elcogen Oy, Finland.…”

Section: Methodsmentioning

confidence: 99%

“…A process diagram of the 6 kW demonstration SOFC power unit utilized in the THDA algorithm system level evaluation experiments .…”

Section: Methodsmentioning

confidence: 99%

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Online Total Harmonic Distortion Analysis for Solid Oxide Fuel Cell Stack Monitoring in System Applications

2018

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The applicability of online total harmonic distortion analysis (THDA) for the operation and monitoring of solid oxide fuel cell (SOFC) power systems is investigated by experiments as well as analysis of the algorithm, with a focus on the relationship between stack fuel utilization rate and the corresponding THD index. An online THDA algorithm is implemented in a programmable logic controller (PLC) and operated with a 6 kW SOFC power system demonstration unit. Laboratory experiments on the solid oxide fuel cell technology are carried out to determine key parameters for the algorithm. The embedded implementation of the THDA algorithm, including several modifications to reduce its computational load, and its parameterization is analyzed and the program code is given in the Appendix. The experimental results verify that THDA can be a reliable means for quantitatively monitoring the fuel utilization rate experienced by the stack and that the algorithm is simple to implement on an embedded controller.

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Section: Thda Algorithm Verification In System Environmentsupporting

confidence: 62%

Section: Methodsmentioning

confidence: 99%

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Online Total Harmonic Distortion Analysis for Solid Oxide Fuel Cell Stack Monitoring in System Applications

2018

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“…Four independent system inputs, given in Table 1, were manipulated according to a so‐called fractional factorial experiment design. (See 25 for details on fractional factorial experiments in general and 28 for further information on the particular experiments.) The fractional factorial design was adopted as previous work 23, 24 proved that interactions of the previously investigated inputs ($ {x_1} - {x_3} $ ) is insignificant and thus the number of experimental conditions could be decreased.…”

Section: Methodsmentioning

confidence: 99%

Application of Multivariable Regression Model for SOFC Stack Temperature Estimation in System Environment

et al. 2015

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The applicability of multivariable linear regression (MLR) models to estimate the maximum temperature inside a SOFC stack is investigated experimentally. The experiments were carried out with a complete 10 kW SOFC system. The behavior of the maximum temperature measured inside a SOFC stack with respect to four independent input variables (stack current, air flow, air inlet temperature and fuel flow) is examined following the design of experiments methodology, and MLR models are created based on the retrieved data. The practical feasibility of the MLR estimate is investigated experimentally with the 10 kW system by evaluating the accuracy of the estimate in two test cases: (i) a system load change where the stack temperature is regulated by a closedloop controller using the MLR estimate and (ii) during operator-imposed disturbances in the fuel system (a variation in the methane conversion in the fuel pre-reformer). Finally, the performance of the MLR estimate is evaluated with another, 64-cell stack operated at higher current density.

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“…Nevertheless, the high computational complexity of neural network is a barrier to the practical situation. A high-order ARX model is also established in Vassilios A and Thomas et al, 17 where extended Kalman filter (EKF) is implemented to estimate solid oxide fuel cell stack temperature. The simulation results indicate that this method could get an accurate real-time estimation.…”

Section: Introductionmentioning

confidence: 99%

Temperature distribution estimation via data-driven model and adaptive Kalman filter in modular data centers

Jiang

Shi

Moazanigoodarzi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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With the rapid development of information and communications technology, increasing number of data centers is required to support the cloud computing, and critical web-based services that run our daily lives. The conventional cloud data centers usually adopt computer room air conditioner or inRow units as the cooling sytem, while the rack mountable cooling unit is a more promising equipment due to the economy, exact controllability, flexibility, and scalability. To ensure the efficiency of control system in rack mountable cooling unit and the security of servers in the data centers, the information of temperature distribution is very essential. Basically, the temperature distribution could be obtained through physical sensors easily. However, considering the cost of whole system and the burden of fault diagnosis in sensor networks, the number of temperature sensors should be kept down to a bare minimum. Therefore, it is necessary to develop an effective and real-time observer to estimate the temperature distribution in the system. Besides, due to the complex air flow and heat transfer in the container, it is quite difficult to construct a physics model. To this end, a novel observer embracing data-driven model and adaptive Kalman filter is proposed in this work. Auto regression exogenous model is adopted as the framework of data-driven model, and the model is identified through a algorithm of partial least square. Moreover, to represent the nonlinear behaviors in the system, fuzzy c-means is applied for data classification and getting multiple local linear models. Finally, adaptive Kalman filter is utilized to estimate the temperature distribution on the basis of proposed data-driven model. The estimation results based on experimental data indicate the performance of proposed approach is remarkable.

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Solid oxide fuel cell stack temperature estimation with data-based modeling – Designed experiments and parameter identification

Cited by 36 publications

References 18 publications

Online Total Harmonic Distortion Analysis for Solid Oxide Fuel Cell Stack Monitoring in System Applications

Online Total Harmonic Distortion Analysis for Solid Oxide Fuel Cell Stack Monitoring in System Applications

Application of Multivariable Regression Model for SOFC Stack Temperature Estimation in System Environment

Temperature distribution estimation via data-driven model and adaptive Kalman filter in modular data centers

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