Adaptive operation strategy for voltage stability enhancement in active DMFCs

Yang, Qinwen; Hu, Xu-Qu; Lei, Xiu-Cheng; Zhu, Ying; Wang, Xingyi; Ji, Shengcheng

doi:10.1016/j.enconman.2018.04.110

Cited by 23 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, in figure 1 (b) shows the peak of C at region 26.703° and Pt at 54.67°. In addition, peaks also appear in the area around 12° -20° which indicates the peak of PTFE and nafion [9]. Decreasing Pt atomic intensity is seen in the results of XRD Pt-Ru/C electrodes.…”

Section: Results and Discussion Electrode Characterization Using Xrdmentioning

confidence: 85%

Performance Test of Membrane Electrode Assembly in DAFC using Mixed Methanol and Ethanol Fuel with Various Volume Comparison

Yulianti

Rohendi

Syarif

et al. 2019

IJFAC

View full text Add to dashboard Cite

Direct Alcohol Fuel Cell (DAFC) performance is influenced by electrocatalysis reactions that occur in Membrane Electrode Assembly (MEA). In this study, MEA was made with Pt-Ru/C (anode) and Pt/C (cathode) catalysts. The results of the electrode characterization with XRD showed a carbon peak at 26.63° and Ru at 40.58°. Based on the results of Cyclic Voltammetry (CV) measurements, the Electrochemical Surface Area (ECSA) electrode value is known to be 373.601 cm 2 /mg. Meanwhile, the impedance value is 4.315 Ω and the electric al conductivity value is 6.61x10-4 S/cm. MEA testing using MeOH 3 M fuel produces Open Circuit Voltage (OCV) of 0.650 V. Meanwhile, MEA performance testing uses a mixture of methanol and ethanol 2 M in loading conditions obtained the best mixture of fuel composition is methanol: ethanol = 90:10 with a maximum power density of 4.34 mW/cm 2 and is able to maintain the voltage at 0.649 V under conditions of 6.875 mA/cm 2. The results also showed that the volume of ethanol which was too high resulted in a decrease in cell performance in the fuel mixture caused by the competition of adsorption between competing methanol and ethanol occupying the active site of the catalyst.

show abstract

Section: Results and Discussion Electrode Characterization Using Xrdmentioning

confidence: 85%

Performance Test of Membrane Electrode Assembly in DAFC using Mixed Methanol and Ethanol Fuel with Various Volume Comparison

Yulianti

Rohendi

Syarif

et al. 2019

IJFAC

View full text Add to dashboard Cite

show abstract

“…The phenomenon of cathodic mixed potential, which is related to methanol crossover is accounted for as well by the DMFC model. Adaptive operations for voltage stabilization are proposed in [23] by using a simplified semi-empirical model combined to an on-demand control system. A rapid response of DMFC (less than 5 s) to variations of operating parameters is experimentally observed, ensuring the applicability of the proposed adaptive control strategy.…”

Section: Dmfc Semi-empirical Modelsmentioning

confidence: 99%

Distributed and Lumped Parameter Models for Fuel Cells

Guarnieri¹,

Alotto²,

Moro³

2020

Thermodynamics and Energy Engineering

View full text Add to dashboard Cite

The chapter presents a review of modeling techniques for three types of fuel cells that are gaining industrial importance, namely, polymer electrolyte membrane (PEMFC), direct methanol (DMFC), and solid oxide (SOFC) fuel cells (FCs). The models presented are both multidimensional, suitable for investigating distributions, gradients, and inhomogeneities inside the cells, and zero-dimensional, which allows for fast analyses of overall performance and can be easily interfaced with or embedded in other numerical tools, for example, for studying the interaction with static converters needed to control the electric power flow. Thermal dependence is considered in all models. Some special numerical approaches are presented, which allow facing specific problems. An example is the Proper Generalized Decomposition (PDG) that allows overcoming the challenges arising from the extreme aspect ratio of the thin electrolyte separating anode and cathode. The use of numerical modeling as part of identification techniques, particularly by means of stochastic optimization approaches, for extracting the material parameters from multiple in situ measurements is also discussed and examples are given. Merits and demerits of the different models are discussed.

show abstract

“…In this paper, we use the Correlation-based Feature Selection (CFS) method [22][23][24] for feature selection. This method adopts a correlation-based heuristic to evaluate the value of features.…”

Section: Feature Selectionmentioning

confidence: 99%

A hierarchical ensemble learning framework for energy-efficient automatic train driving

Zhao

Liu

et al. 2019

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters (e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging -REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.

show abstract

Adaptive operation strategy for voltage stability enhancement in active DMFCs

Cited by 23 publications

References 41 publications

Performance Test of Membrane Electrode Assembly in DAFC using Mixed Methanol and Ethanol Fuel with Various Volume Comparison

Performance Test of Membrane Electrode Assembly in DAFC using Mixed Methanol and Ethanol Fuel with Various Volume Comparison

Distributed and Lumped Parameter Models for Fuel Cells

A hierarchical ensemble learning framework for energy-efficient automatic train driving

Contact Info

Product

Resources

About