Semi-physical modeling and control of a centrifugal compressor for the air feeding of a PEM fuel cell

Zhao, Dongdong; Xu, Liangcai; Huangfu, Yigeng; Dou, Manfeng; Liu, Jianxing

doi:10.1016/j.enconman.2017.11.030

Cited by 91 publications

(23 citation statements)

References 32 publications

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“…Zhang et al (2017) used a one-dimensional dynamic simulation model of air supply system established in the simulation environment to analyse the performance of a fuel cell air supply system. Zhao et al (2017) presented a semi-physical centrifugal compressor model using state-space equations and interior-point method, which have good agreement with the experimental data. Hering et al (2018) designed and implemented several approaches for the characteristic map modelling of the blower in a 10 kW SOFC system based on the blower laws of similitude and different regression models.…”

Section: Introductionmentioning

confidence: 69%

WITHDRAWN: Performance Modelling of the Regenerative Blower for Air Supply of SOFC System with Random Forest and Bayesian Optimization

Zhao

Wu²,

Xiong

et al. 2020

Preprint

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Abstract Air starvation is a serious hazard during the operation of a solid oxide fuel cell (SOFC) system. So, an accurate performance model of the air supply system is needed for the control of air flow rate and the reduction of air starvation. The performance modelling of a regenerative blower, which is used for the air supply of a 5 kW SOFC system during power generation, is proposed based on the methods of random forest (RF) and Bayesian Optimization (BO). The performance model is first built through the RF method. The BO algorithm is then used to adjust the hyper-parameters of the model. The developed model can predict the outlet air flow rate through the operation data of regenerative blower, i.e. the rotation speed of impeller, the inlet and outlet pressure, humidity and temperature, without specific geometrical parameters. A series of operation data obtained from an experimental test rig are adopted to train and validate the presented method. Finally, the prediction performance of the developed algorithm is compared with different machine learning methods, such as decision tree regression, deep neural network. Results show that the proposed method can predict the outlet air flow rate under different working conditions with minimum prediction errors within 3.5% and acceptable computational cost. Therefore, the developed algorithm is promising for performance modelling of the blower, which can be used to predict the outlet flow rate and control the air supply system at SOFC in our next research.

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Section: Introductionmentioning

confidence: 69%

WITHDRAWN: Performance Modelling of the Regenerative Blower for Air Supply of SOFC System with Random Forest and Bayesian Optimization

Zhao

Wu²,

Xiong

et al. 2020

Preprint

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“…6 − 8 Meanwhile, high system efficiency and excellent low-temperature performance promote the applications of PEM fuel cells to automotive. 9 It was demonstrated that the energy consumed by air compressors reached 15% of the total energy in fuel cell systems. 10 Therefore, high efficiency air compressors should be adopted to decrease the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Investigations of an Opposed Rotary Piston Compressor for the Air Feeding of a Polymer Electrolyte Membrane Fuel Cell System

et al. 2020

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Automotive polymer electrolyte membrane fuel cell systems are attracting much attention, driven by the requirements of low automotive exhaust emissions and energy consumption. A polymer electrolyte membrane fuel cell system provides opportunities for the developments in different types of air compressors. This paper proposed an opposed rotary piston compressor, which had the merits of more compact structures, less movement components, and a high pressure ratio, meeting the requirements of polymer electrolyte membrane fuel cell systems. Preliminary performance evaluations of the opposed rotary piston compressor were conducted under various scenarios. This will make a foundation for optimizations of outlet pipe layouts of the compressor. A three-dimensional numerical simulation approach was used; further, in-cylinder pressure evolutions, fluid mass flow rates, and P – V diagrams were analyzed. It indicated that the cyclic period of the opposed rotary piston compressor was half of reciprocating piston compressors. The specific mass flow rate of the compressor is in the range of 0.094–0.113 kg·(s·L) −1 for the given scenarios. Outlet ports 1 and 2 dominated the mass flow in the discharge process under scenarios 1, 3, and 4. In-cylinder pressure profiles show multipeaks for all of these scenarios. In-cylinder pressure increased rapidly in the compression process and part of the discharge process, which led to high energy consumption and low adiabatic efficiency. The maximum adiabatic efficiency is approximately 43.96% among the given scenarios.

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“…In that case, the operating temperature should be controlled at an appropriate range . With the increase of current density, the oxygen and hydrogen consumption will be increased which leads to the increase of reactants mass flow rates . During the operation of PEM fuel cell, ohmic losses were a main factor that leads to the diminishment of the output power.…”

Section: Research Problemmentioning

confidence: 99%

“…25 With the increase of current density, the oxygen and hydrogen consumption will be increased which leads to the increase of reactants mass flow rates. 26,27 During the operation of PEM fuel cell, ohmic losses were a main factor that leads to the diminishment of the output power. Humidity was an important parameter to decrease the membrane electrical resistance and therefore the ohmic losses, so an appropriate range of humidity was required during the operation of PEM fuel cell.…”

Section: Research Problemmentioning

confidence: 99%

A coupled and interactive influence of operational parameters for optimizing power output of cleaner energy production systems under uncertain conditions

et al. 2019

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Summary The mechanisms in proton‐exchange membrane fuel cells (PEMFCs) cannot be explicitly represented by a mathematical function because the PEMFC system is multi‐dimensional and complex and represents uncertainty in operation variables, which cannot be modeled by experiments or by trial‐and‐error approach. Therefore, this work proposes to study the coupled and interactive influence of stack current (SC), stack temperature (ST), oxygen excess ratio (OER), hydrogen excess ratio (HER), and inlet air humidity (IAH) for optimizing the power output of PEMFC. The data obtained from the experiments have been inserted into architecture of automated neural‐network search, which automates the selection of error function, activation function, uncertainties in inputs and number of hidden neurons in formulation of a robust and accurate model for power density as a function of five operational variables. Among the operational variables, the correlation coefficient between the SC and the output power is the highest, followed by OER, and the ST. However, for HER and IAH, the power output follows negative nonlinear relation. The optimization converged at 130th iteration results in maximum power output of 3410 W for an optimum value of SC (51A), ST (59°C), OER (3:2), HER (1:10), and IAH (0.8).

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Semi-physical modeling and control of a centrifugal compressor for the air feeding of a PEM fuel cell

Cited by 91 publications

References 32 publications

WITHDRAWN: Performance Modelling of the Regenerative Blower for Air Supply of SOFC System with Random Forest and Bayesian Optimization

WITHDRAWN: Performance Modelling of the Regenerative Blower for Air Supply of SOFC System with Random Forest and Bayesian Optimization

Preliminary Investigations of an Opposed Rotary Piston Compressor for the Air Feeding of a Polymer Electrolyte Membrane Fuel Cell System

A coupled and interactive influence of operational parameters for optimizing power output of cleaner energy production systems under uncertain conditions

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