Multi-objective optimization of PEM fuel cell by coupled significant variables recognition, surrogate models and a multi-objective genetic algorithm

Li, Hongwei; Xu, Boshi; Lu, Guolong; Du, Changqing; Huang, Na

doi:10.1016/j.enconman.2021.114063

Cited by 138 publications

(34 citation statements)

References 38 publications

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“…This has become a significant issue for the Iranian energy market [11][12][13][14][15][16][17]. Traditional fossil fuel-based power plants are not able to provide the power demand [17][18][19][20][21][22][23][24], and blackouts are inevitable, especially in the hot months of the year [25][26][27][28][29][30][31][32][33]. Meanwhile, renewable energy sources (RESs) are one of the efficient solutions for dealing with such an important issue [34][35][36][37][38][39][40][41][42][43].…”

Section: Introduction 1motivationsmentioning

confidence: 99%

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

et al. 2021

Energies

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The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

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Section: Introduction 1motivationsmentioning

confidence: 99%

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

et al. 2021

Energies

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“…In the 1980s, when Cheney studied the optimal structure of variable-thickness plates, he set the objective function as the maximum stiffness, got a variety of ribbed structures and found that the ribs can improve the structure, another one set the minimum flexibility as the objective function while optimizing the variable thickness, and set the corner base size of the finite element as the design variable, established the finite element method (Ma et al, 2021). At the end of the last century, Kauai established an optimized genetic algorithm model, which is now used in material topology optimization design (Li et al, 2021).…”

Section: Research Status Of Structural Optimizationmentioning

confidence: 99%

Simulation and optimization of a new energy vehicle power battery pack structure

Ruan¹,

Yu²,

Hu³

et al. 2021

Journal of Theoretical and Applied Mechanics

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With the rapid growth in new energy vehicle industry, more and more new energy vehicle battery packs catch fire or even explode due to the internal short circuit. Comparing with traditional vehicles, the new energy vehicles industry should pay more attention to safety of power battery pack structures. The battery pack is an important barrier to protect the internal batteries. A battery pack structure model is imported into ANSYS for structural optimization under sharp acceleration, sharp turn and sharp deceleration turn conditions on the bumpy road. Based on the simulation, the battery pack structure is improved, and suitable materials are determined. Then the collision resistance of the optimized battery pack is verified, and the safety level is greatly improved. While ensuring the safety and reliability of the battery pack structure, it also reduces the weight to satisfy the lightweight design and complies with relevant technical standards.

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“…To avoid initial parameter determination, intelligent optimization algorithms are more appropriate for online identification. The genetic algorithm (GA) is an evolutionary algorithm that imitates the evolution of a population, which mainly includes coding, fitness calculation, selection, crossover, and mutation steps [43]. The algorithm first needs to encode the target parameters and initialize individuals to construct the initial population, then combine the target problem to calculate fitness calculation of the corresponding chromosomal individuals, and on this basis, select the outstanding individuals with high fitness to complete the genetics operations such as crossover and mutation.…”

Section: Hybrid Genetic Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

Xie¹,

Wang

Zhu³

et al. 2021

Energies

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The reliability and durability of the proton exchange membrane (PEM) fuel cells are vital factors restricting their applications. Therefore, establishing an online fault diagnosis system is of great significance. In this paper, a multi-stage fault diagnosis method for the PEM fuel cell is proposed. First, the tests of electrochemical impedance spectroscopy under various fault conditions are conducted. Specifically, prone recoverable faults, such as flooding, membrane drying, and air starvation, are included, and different fault degrees from minor, moderate to severe, are covered. Based on this, an equivalent circuit model (ECM) is selected to fit impedance spectroscopy by the hybrid genetic particle swarm optimization algorithm, and then fault features are determined by the analysis of each model parameter under different fault conditions. Furthermore, a multi-stage fault diagnosis model is constructed with the support vector machine with the binary tree, in which fault features obtained from the ECM are used as the characteristic inputs to realize the fault classification (including fault type and fault degree) online. The results show that the accuracy of the basic fault test and subdivided fault test can reach 100% and 98.3%, respectively, which indicates that the proposed diagnosis method can effectively identify flooding, drying, and air starvation of PEM fuel cells.

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Multi-objective optimization of PEM fuel cell by coupled significant variables recognition, surrogate models and a multi-objective genetic algorithm

Cited by 138 publications

References 38 publications

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Simulation and optimization of a new energy vehicle power battery pack structure

A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

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