Comprehensive vibro-acoustic characteristics and mathematical modeling of electric high-speed centrifugal compressor surge for fuel cell vehicles at various compressor speeds

Chen, Siyue; Zuo, Shuguang; Wu, Zhipeng; Liu, Chang

doi:10.1016/j.ymssp.2022.109311

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…The structure of the PEMFC system is very complex and mainly contains the cell stack and subsystems, which can be divided into five major subsystems: the fuel delivery subsystem, air supply subsystem that feeds the cathode with oxidant, water management subsystem, thermal management subsystem, and power output subsystem [3,4]. The performance of the PEMFC is related to various operating parameters, among which oxygen must be supplied in sufficient quantities to ensure electrochemical reaction and maximum net power [5,6]. Typically, the oxygen content is expressed as the oxygen excess ratio (OER) which is defined as the oxygen flow rate to the amount of oxygen consumed by the cell stack [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

Zhang,

Zhang

et al. 2023

Fuel Cells

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To address the challenge of tracking the optimal oxygen excess ratio (OER) in the cell stack cathode of the proton exchange membrane fuel cell (PEMFC), which is subject to norm‐bounded parameter uncertainty and frequent external perturbations, this manuscript provides a novel robust model predictive control strategy with a state feedback control structure and hard constraints. We aim to minimize the tracking error of OER while maximizing the net power under varying operational conditions of the PEMFC. Initially, a control‐oriented model for the air supply system is built and its accuracy is validated by comparing it with experimental results. Next, we construct the discrete state‐space equations for optimal control and employ the linear matrix inequality (LMI) method to transform the problem of difficult min‐max optimization solutions into a convex optimization problem with LMI constraints. Finally, the effectiveness of the proposed control algorithm is validated through simulation. The results demonstrate the strong robustness and superiority of the proposed controller against uncertainties in plant parameters for robust OER trajectory tracking. It improves the transient performance through robust precision tracking applications, which will eventually accelerate PEMFC commercialization.

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Section: Introductionmentioning

confidence: 99%

Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

Zhang,

Zhang

et al. 2023

Fuel Cells

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“…Turbochargers are widely used in the transportation field because they increase engines' specific power and reduce gas emissions [4][5][6][7]. Unfortunately, the noise emissions generated by turbochargers become a non-negligible part of the engine noise source, hindering environmentally sustainable development to some extent [8,9]. In addition, due to the increase in output power requirements for diesel engines, the turbocharger pressure ratio increases, resulting in an increase in the compressor load and higher aerodynamic noise emissions [10,11].…”

Section: Introductionmentioning

confidence: 99%

An Explainable Prediction Model for Aerodynamic Noise of an Engine Turbocharger Compressor Using an Ensemble Learning and Shapley Additive Explanations Approach

Huang,

Ni,

Qiao

et al. 2023

Sustainability

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In the fields of environment and transportation, the aerodynamic noise emissions emitted from heavy-duty diesel engine turbocharger compressors are of great harm to the environment and human health, which needs to be addressed urgently. However, for the study of compressor aerodynamic noise, particularly at the full operating range, experimental or numerical simulation methods are costly or long-period, which do not match engineering requirements. To fill this gap, a method based on ensemble learning is proposed to predict aerodynamic noise. In this study, 10,773 datasets were collected to establish and normalize an aerodynamic noise dataset. Four ensemble learning algorithms (random forest, extreme gradient boosting, categorical boosting (CatBoost) and light gradient boosting machine) were applied to establish the mapping functions between the total sound pressure level (SPL) of the aerodynamic noise and the speed, mass flow rate, pressure ratio and frequency of the compressor. The results showed that, among the four models, the CatBoost model had the best prediction performance with a correlation coefficient and root mean square error of 0.984798 and 0.000628, respectively. In addition, the error between the predicted total SPL and the observed value was the smallest, at only 0.37%. Therefore, the method based on the CatBoost algorithm to predict aerodynamic noise is proposed. For different operating points of the compressor, the CatBoost model had high prediction accuracy. The noise contour cloud in the predicted MAP from the CatBoost model was better at characterizing the variation in the total SPL. The maximum and minimum total SPLs were 122.53 dB and 115.42 dB, respectively. To further interpret the model, an analysis conducted by applying the Shapley Additive Explanation algorithm showed that frequency significantly affected the SPL, while the speed, mass flow rate and pressure ratio had little effect on the SPL. Therefore, the proposed method based on the CatBoost algorithm could well predict aerodynamic noise emissions from a turbocharger compressor.

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“…In recent years, high-speed centrifugal compressors have garnered significant attention, benefiting from the rapid development of fuel cell vehicles. Centrifugal compressors for fuel cell vehicles have a number of characteristics: they are electrically driven, two-stage, and oil-free, with power ranging from 5 to 50 kW and speeds of tens to hundreds of thousands of revolutions per minute [1][2][3][4][5][6]. Their primary function is to supply clean, high-pressure gas to the fuel cell.…”

Section: Introductionmentioning

confidence: 99%

Impact of Gas Foil Bearings, Labyrinth Seals, and Impellers on the Critical Speed of Centrifugal Compressors for Fuel Cell Vehicles: A Comprehensive Investigation

Ying,

Liu,

Zhang

et al. 2023

Lubricants

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The critical speed is a crucial factor that impacts the stability of high-speed compressors. However, limited research has simultaneously considered the influence of gas foil bearings (GFBs), labyrinth seals, and impellers on critical speed. In this study, we develop a rotordynamic model that incorporates the aerodynamic forces of GFBs, labyrinth seals, and impellers to explore the effects of each component on the critical speed. To validate the developed model, experimental tests are conducted on a centrifugal compressor test bed, and the results exhibit a high level of agreement with the model calculations. By comparing the model calculations that include different components, we comprehensively analyze the influence of each component on the critical speed. The findings reveal that, for centrifugal compressors used in fuel cell vehicles, the rotordynamic coefficients resulting from GFBs are significantly larger than those resulting from impellers and labyrinth seals. Thus, it is reasonable to disregard the aerodynamic forces caused by impellers and labyrinth seals when calculating the critical speed. Furthermore, substituting rigid gas bearings for GFBs as a means to simplify the calculations has only a very slight impact on the results. This study aims to optimize the design process of centrifugal compressors for fuel cell vehicles and offers valuable insights for designing compressors of similar sizes.

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Comprehensive vibro-acoustic characteristics and mathematical modeling of electric high-speed centrifugal compressor surge for fuel cell vehicles at various compressor speeds

Cited by 8 publications

References 43 publications

Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

An Explainable Prediction Model for Aerodynamic Noise of an Engine Turbocharger Compressor Using an Ensemble Learning and Shapley Additive Explanations Approach

Impact of Gas Foil Bearings, Labyrinth Seals, and Impellers on the Critical Speed of Centrifugal Compressors for Fuel Cell Vehicles: A Comprehensive Investigation

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