scholarly journals Fractional-Order Modeling and Parameter Identification for Ultracapacitors with a New Hybrid SOA Method

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4251 ◽  
Author(s):  
Jianhua Guo ◽  
Weilun Liu ◽  
Liang Chu ◽  
Jingyuan Zhao
Keyword(s):  
Optimization Algorithm ◽  
Impedance Spectrum ◽  
Time Behavior ◽  
Distribution Dynamics ◽  
Identification Procedure ◽  
Fractional Model ◽  
Hybrid Optimization Algorithm ◽  
Constant Phase Elements ◽  
Seeker Optimization Algorithm ◽  
Different Levels

This paper deals with an ultracapacitor (UC) model and its identification procedure. To take UC’s fractional characteristic into account, two constant phase elements (CPEs) are used to construct a model structure according to impedance spectrum analysis. The different behaviors of UC such as capacitance, resistance, and charge distribution dynamics are simulated by the corresponding part in the model. The resistance under different voltages is calculated through the voltage rebound method to explore its non-linear characteristics and create a look-up table. A nonlinear fractional model around an operation voltage is then deduced by applying the resistance table. This time identification is carried by a proposed hybrid optimization algorithm: Nelder-Mead seeker algorithm (NMSA), which embeds the Nelder–Mead Simplex (NMS) method into the seeker optimization algorithm (SOA). Its time behavior has been compared with the linear fractional model for charging and discharging current profiles at different levels.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

Optimization Design of Drawbead in Drawing Tools of Autobody Cover Panel

2002 ◽  
Vol 124 (2) ◽  
pp. 278-285 ◽  
Author(s):  
Gang Liu ◽  
Zhongqin Lin ◽  
Youxia Bao
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Experimental Results ◽  
Hybrid Optimization ◽  
Force Model ◽  
Hybrid Optimization Algorithm ◽  
Design Plan ◽  
Formed Part ◽  
Restraining Force

In the tooling design of autobody cover panels, design of drawbead will affect the distribution of drawing restraining force along mouth of dies and the relative flowing velocity of the blank, and consequently, will affect the distributions of strain and thickness in a formed part. Therefore, reasonable design of drawbead is the key point of cover panels’ forming quality. An optimization design method of drawbead, using one improved hybrid optimization algorithm combined with FEM software, is proposed in this paper. First, we used this method to design the distribution of drawbead restraining force along the mouth of a die, then the actual type and geometrical parameters of drawbead could be obtained according to an improved drawbead restraining force model and the improved hybrid optimization algorithm. This optimization method of drawbead was used in designing drawing tools of an actual autobody cover panel, and an optimized drawbead design plan has been obtained, by which deformation redundancy was increased from 0% under uniform drawbead control to 10%. Plastic strain of all area of formed part was larger than 2% and the minimum flange width was larger than 10 mm. Therefore, not only better formability and high dent resistance were obtained, but also fine cutting contour line and high assembly quality could be obtained. An actual drawing part has been formed using the optimized drawbead, and the experimental results were compared with the simulating results in order to verify the validity of the optimized design plan. Good agreement of thickness on critical areas between experimental results and simulation results proves that the optimization design method of drawbead could be successfully applied in designing actual tools of autobody cover panels.

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