Data-driven oxygen excess ratio control for proton exchange membrane fuel cell

Sun, Li; Shen, Jiong; Hua, Qingsong; Lee, Kwang Y.

doi:10.1016/j.apenergy.2018.09.036

Cited by 195 publications

(102 citation statements)

References 27 publications

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“…Using FLUENT 6.3, the governing equations for the two-dimensional ice solidification process in the STIS device are solved. The QUICK scheme is a high-order differencing scheme, which can be used to solve Equations (2), (3) and (9) in the present model. The iteration in each time step stops once the average residual of the temperature is below one millionth.…”

Section: Numerical Solutionsmentioning

confidence: 99%

“…For example, the energy load in urban areas varies more than 30% between day and night, which leads to lots of energy waste [4][5][6]. To fix these problems, thermal energy storage systems are attracting more attention [7][8][9]. With phase change materials (PCMs) they can absorb energy in the evening and regenerate the thermal energy in the daytime, which fills the gap of the energy load between day and night [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device

et al. 2020

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The theoretical model of the solidification process of a shell-and-tube ice storage (STIS) device with longitudinal fins is established. The liquid fraction, the energy-discharging rate and the ice storage ratio are investigated, with particular focus on the effects of the fin structure parameters on the solidification process. Furthermore, the temperature and the streamline distributions are discussed to reveal the mechanism of the solidification process in the STIS device and the negative effect of natural convection (NC). It is indicated that the solidification process of the STIS device is dominated by the heat transfer via the fins at the beginning, and then by the heat transfer at the water-ice interface. The ice storage is negatively affected by the NC, for the reason that the water with a higher temperature stays in the lower part of the STIS device and the temperature gradient at the water-ice interface is small. The ice storage performance can be enhanced by increasing the fin structure parameters, including height, thickness and number.

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Section: Numerical Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device

et al. 2020

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“…A Cm ij and A line ij express the availability of current measurement and transmission line at line ij (A Cm ii = 1, A line ii = 1). Where: A Vm j = (A PT i ) 3 and A Cm ij = (A CT i ) 3 , for three PTs and CTs are needed to measure corresponding phasor values, which are in series [28]. It is noted that when line outages are not considered in the OPP problem, the availability of transmission lines is regarded as 1.…”

Section: Possibility Of Observability Without Line Outagementioning

confidence: 99%

“…Apparently, the developed placement model can provide solutions with higher reliability. ,11,12,15,19,20,22,24,26,28,29,30,31, 32,33,35,36,37,38,41,45,46,47,50,51,53,54,56,57 Multi-objective 29 0.00180 1,3,4,6,9,11,12,15,19,20,22,24,27,29,30,32,33,35,36,39,41,44,46,47,49,51,53,55,57 Single-objective 29 0.00298 1,3,5,7,9,12,14,18,20,22,24,27,29,30,32,33,35,38,39,40,42,43,45,47,…”

Section: Numerical Studymentioning

confidence: 99%

“…The wide-area measurement system (WAMS) was developed to provide real-time data for state estimator with high precision and great efficiency, in order to further guarantee the power system stability [1] and meet the requirement of a rapid increase in energy demand [2,3]. Phasor measurement units (PMU) can measure voltage phasor at the bus as well as current phasors of incident branches, thereby guaranteeing the observability of the bus where it is located and its adjacent buses [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

Huang

Liu

et al. 2019

Sustainability

Self Cite

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Optimal phasor measurement units (PMU) placement was developed to determine the number and locations of PMUs on the premise of full observability of the whole network. In order to enhance reliability under contingencies, redundancy should also be considered beside the number of PMUs in optimal phasor measurement units placement problem. Thus, in this paper, a multi-objective model was established to consider the two conflicting components simultaneously, solved by ε-constraint method and the fuzzy satisfying approach. The redundancy here was formulated as average possibility of observability including random component outages, and full possibility formula was applied to calculate the average possibility of observability in the case of single line outage. Finally, the model was employed to the IEEE-57 bus system, and the results verified that the developed model could provide a placement scheme with higher reliability.Sustainability 2019, 11, 7097 2 of 12 bee colony (ABC) algorithm, modified binary cuckoo optimization algorithm (MBCOA), weighted method, multi-objective evolutionary algorithm-based, were adopted to solve the multi-objective model in [11][12][13][14][15], respectively. In addition, to obtain the final solution from the Pareto front quickly and accurately, the fuzzy satisfying approach was applied in [16,17] and sparse neighborhood surroundings in [18]. Furthermore, the accuracy of state estimation was taken into consideration, subject to the constraint of supervisory control and data acquisition (SCADA) measurement data in [19] and the uncertainties of power system conditions in [20].Literatures aforementioned analyzed the system observability from a deterministic view, including two contrary states, i.e., observable or unobservable. However, it was investigated in a probabilistic manner in [21][22][23][24]. In [21], to obtain a PMU placement scheme with unobservable probability resulted from 1st-order and 2nd-order failures of PMUs and line less than the pre-defined threshold, the unobservable probability was calculated iteratively by increasing PMU numbers gradually on the basis of initial PMU placement scheme, until the requirements were met. A multi-objective model was proposed, simultaneously minimizing the PMU cost and unobservable probability under PMU or line failures in [22]. However, random component outages were not taken into consideration in [21] and [22]. In [23], a probabilistic manner was adopted to differentiate the multiple solutions and showed significant priority comparing to measurement redundancy. A solution with maximum observable probability considering random component outages was selected as the final optimal scheme. In addition, when considering multistage PMU placement, the observable probability was regarded as the objective to optimize at each stage [24].In this paper, firstly, APO of all buses in the network was calculated under two different cases, i.e., with and without consideration of single line outages. It is noticeable to consider the effect of single line outag...

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Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge‐ and Choke‐Constrained Awareness

Ye,

Zhong,

Shen

et al. 2024

Advcd Theory and Sims

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The efficiency and dynamic response of air compressors are crucial for stability and lifespan of hydrogen fuel cells. A predictive control scheme with surge‐ and choke‐constrained awareness is proposed to ensure safe and efficient operation of air compressors in this study. The proposed scheme consists of an efficiency enhancement model predictive control (EE‐MPC), and an improved active disturbance rejection control (IADRC). Surge‐ and choke‐constrained awareness is achieved by comparing predicted air flow with surge and choke limitations. Simultaneously, the EE‐MPC is constrained with oxygen excess ratio (OER) and obtains optimal solution by searching active set. The reference flow and supply manifold pressure trajectories for IADRC are generated by EE‐MPC. A designed piecewise differentiable nonlinear smoothing function is embedded in IADRC. The disturbances are estimated for coordinating flow and pressure control. Under China heavy‐duty commercial vehicle test cycle for bus conditions, root‐mean‐squared errors (RMSEs) of flow and pressure are 3.27 g s−1 and 1.88 × 103 Pa, respectively, and the mean efficiency can be enhanced by 13.4% compared to the MPC with fixed OER. Finally, a controller hardware‐in‐the‐loop test is conducted, with flow and pressure RMSEs of 2.48 g s−1 and 4.28 × 103 Pa between the test and simulation, respectively.

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Data-driven oxygen excess ratio control for proton exchange membrane fuel cell

Cited by 195 publications

References 27 publications

Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device

Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device

A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge‐ and Choke‐Constrained Awareness

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