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engineering optimization approach of nonaqueous electrolyte for sodium ion battery with long cycle life and safety

Green Energy & Environment 2021年第2期6卷 212-219页

作者： Haiying Che Xinrong Yang Yan Yu Chaoliang Pan Hong Wang Yonghong Deng Linsen Li Zi-Feng Ma Shanghai Electrochemical Energy Devices Research Center Depurment of Chemical EngineeringShanghai Jiao Tong UniversitygShanghai200240China Zhejiang NaTRIUM Energy Co.Ltd. Shaoxing 312000China Shanghai Zjian Chemical Technology Co.Ltd Shanghai200241China Shenzhen CAPCHEM Technology Co.Ltd Shenzhen518027China

Electrolyte design strategies are closely related to the capacities, cycle life and safety of sodium–ion batteries. In this study, we aimed to optimize electrolyte with the focus on engineering aspects. The basic physicochemical properties including ionic conductivity, viscosity,wettability and thermochemical stability of the electrolytes using Na PF6 as the solute and the mixed solvent with different components of EMC,DMC or DEC in PC or EC were systematically measured. Ah pouch cell with NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2)/hard carbon electrodes was used to evaluate the performance of the prepared electrolytes. By using the Inductive Coupled Plasma Emission Spectrometer(ICP), X-ray photoelectron spectroscopy(XPS), Thermogravimetric-differential scanning calorimetry(TG-DSC) and Accelerating Rate Calorimeter(ARC), we show that an optimized electrolyte can effectively promote the formation of a protective interfacial layer on two electrodes, which not only retards parasitic reactions between the electrodes and electrolyte but also suppresses dissolution of metal ions from the cathode. With an optimized electrolyte, a NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2)/hard carbon cell can attain 56.16% capacity retention under the low temperature of -40℃, and can be able to retain 80%capacity retention after more than 2500 cycles while presenting excellent thermal safety.

关键词： Sodium–ion battery NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2) Nonaqueous electrolyte Safety engineering optimization

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for engineering Problems

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Journal of Bionic engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem.Among them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization performance.However,it tends to stagnate prematurely to local optima in solving some specific problems.Therefore,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called CGSE.In this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of SE.To objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with constraints.The final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications.Therefore,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms engineering optimization Spherical evolution algorithm Global optimization

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engineering optimization ON SCALLOP LARVA CULTURE

ENGINEERING OPTIMIZATION ON SCALLOP LARVA CULTURE

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第十八次国际扇贝大会

作者： Ying Liu Jiazheng Shen Institute of Oceanology Chinese Academy of Science 266071

＜正＞Seeding of aquatic product as the basis of aquaculture is becoming more important in aquaculture development.In this study,main problems in seeding production is summarized, then the concept of engineering optimization to seeding culture facilities,culture environment regulations and feed culture is raised to increase the efficiency of seeding production, enhance the stability and controllability of seeding production and promote a healthy and sustainable development of scallop larva production.

关键词： scallop larva culture engineering optimization

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A Web-based engineering optimization System and Its Application

A Web-based Engineering Optimization System and Its Applicat...

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2004 International Symposium on Distributed Computing and Applications to Business, engineering and Science

作者： Caijun Xue Hong Nie Yanqin Dai College of Aerospace engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 P.R.China Qingying Qiu State Key Laboratory of CAD&CG Zhejiang University HangZhou 310027 P.R.China

＜正＞Web-based computing technology has been popular in the engineering fields since it is promising to use computing resources more efficiently.The authors have developed a methodology that takes advantages of the World Wide Web to realize design optimization of an engineering system.A web-based optimization system framework is constructed. JSP/Serverlet technology is used for web server implementation.JAVA JNI programming realizes a link between the serverlets and the optimizers.JAVA JDBC programming realizes to access optimization models in the SQL database server.This paper describes these methodologies in detail.The design optimization of working equipment of a hydraulic excavator is used to illustrate the application of the web-based optimization system.

关键词： engineering optimization Web-based computation JAVA programming JSP/Serverlet

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Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

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Journal of Bionic engineering 2023年第3期20卷 1153-1174页

作者： Xin Wang Xiaogang Dong Yanan Zhang Huiling Chen School of Mathematics and Statistics Changchun University of TechnologyChangchunJilin130012China School of Management Xi’an Jiaotong UniversityXi’anShaanxi710049China Information Construction Office Changchun University of TechnologyChangchunJilin130012China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.

关键词： Harris hawks optimization Bioinspired algorithm Global optimization engineering optimization Feature selection

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An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第1期12卷 142-156页

作者： Wei-Der Chang Department of Computer and Communication Shu-Te University Kaohsiung 824Taiwan

engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)problems.Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system.This paper will focus on the issue about the design problem of NLP with the constrained conditions.The employed method for such NLP problems is a variant of particle swarm optimization(PSO),named improved particle swarm optimization(IPSO).The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems.In this work,many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm.Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

关键词： engineering optimization nonlinear programming constrained conditions improved particle swarm optimization(IPSO)

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