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Extraction of battery parameters using a multi-objective genetic algorithm with a non-linear circuit model

JOURNAL OF POWER SOURCES 2014年 259卷 76-86页

作者： Malik, Aimun Zhang, Zheming Agarwal, Ramesh K. Washington Univ Dept Mech Engn & Mat Sci St Louis MO 63130 USA

There is need for a battery model that can accurately describe the battery performance for an electrical system, such as the electric drive train of electric vehicles. In this paper, both linear and non-linear equivalent circuit models (ECM) are employed as a means of extracting the battery parameters that can be used to model the performance of a battery. The linear and non-linear equivalent circuit models differ in the numbers of capacitance and resistance;the non-linear model has an added circuit;however their numerical characteristics are equivalent. A multi-objective genetic algorithm is employed to accurately extract the values of the battery model parameters. The battery model parameters are obtained for several existing industrial batteries as well as for two recently proposed high performance batteries. Once the model parameters are optimally determined, the results demonstrate that both linear and non-linear equivalent circuit models can predict with acceptable accuracy the performance of various batteries of different sizes, characteristics, capacities, and materials. However, the comparisons of results with catalog and experimental data shows that the predictions of results using the non-linear equivalent circuit model are slightly better than those predicted by the linear model, calculating voltages that are closer to the manufacturers' values. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Battery performance Non-linear equivalent circuit model multi-objective genetic algorithm Parameter extraction

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multi-objective genetic algorithm to Design Manufacturing Process Line Including Feasible and Infeasible Solutions in Neighborhood

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Journal of Mathematics and System Science 2014年第4期4卷 209-219页

作者： Masahiro Arakawa Takumi Wada Department of Civil Engineering and Systems Management Nagoya Institute of Technology Gokiso Showa Nagoya 466-8555Aichi Japan

This paper treats multi-objective problem for manufacturing process design. A purpose of the process design is to decide combinations of work elements assigned to different work centers. multiple work elements are ordinarily assigned to each center. Here, infeasible solutions are easily generated by precedence relationship of work elements in process design. The number of infeasible solutions generated is ordinarily larger than that of feasible solutions generated in the process. Therefore, feasible and infeasible solutions are located in any neighborhood in solution space. It is difficult to seek high quality Pareto solutions in this problem by using conventional multi-objective evolutional algorithms. We consider that the problem includes difficulty to seek high quality solutions by the following characteristics：（1） Since infeasible solutions are resemble to good feasible solutions, many infeasible solutions which have good values of objective functions are easily sought in the search process, （2） Infeasible solutions are useful to select new variable conditions generating good feasible solutions in search process. In this study, a multi-objective genetic algorithm including local search is proposed using these characteristics. Maximum value of average operation times and maximum value of dispersion of operation time in all work centers are used as objective functions to promote productivity. The optimal weighted coefficient is introduced to control the ratio of feasible solutions to all solutions selected in crossover and selection process in the algorithm. This paper shows the effectiveness of the proposed algorithm on simple model.

关键词： Process design process line feasible and infeasible solution multi-objective genetic algorithm mix production simulation

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Optimization of Indoor Air Quality Characteristics in an Air-Conditioned Car Using multi-objective genetic algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第11期39卷 8307-8317页

作者： Thirumal, P. Amirthagadeswaran, K. S. Jayabal, S. Govt Coll Engn Dept Mech Engn Bargur India Govt Coll Technol Dept Mech Engn Coimbatore Tamil Nadu India AC Coll Engn & Technol Dept Mech Engn Karaikkudi Tamil Nadu India

The current investigation is focused on the analysis of indoor air quality characteristics of an air-conditioned car using multi-objective genetic algorithm (MOGA) approach. The conditioned space was selected, and the experiments were planned as per design of experiments to study the effect of human load, fresh air supply and air velocity on the human comfort conditions. The nonlinear regression models were developed to predict the comfort conditions, namely temperature, CO2 level and relative humidity over a specified range of input conditions. The optimum values of indoor air quality responses like Carbon dioxide level below 1,000 ppm, temperature less than 26A degrees C and relative humidity below that of 60% were predicted for input conditions using MOGA in this present investigation.

关键词： Indoor air quality Temperature Relative humidity Carbon dioxide multi-objective genetic algorithm

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Best routes selection in multimodal networks using multi-objective genetic algorithm

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2014年第3期28卷 655-673页

作者： Xiong, Guiwu Wang, Yong Chongqing Univ Sch Econ & Business Adm Chongqing Key Lab Logist Chongqing 400044 Peoples R China

In this study, we propose a bi-level multi-objective Taguchi genetic algorithm for a multimodal routing problem with time windows. The mathematic model is constructed, which is featured by two optimal objectives, multiple available transportation manners and different demanded delivery times. After thoroughly analyzing the characteristics of the formulated model, a corresponding bi-level multi-objective Taguchi genetic algorithm is designed to find the Pareto-optimal front. At the upper level, a genetic multi-objective algorithm simultaneously searches the Pareto-optimal front and provides the most feasible routing path choices for the lower level. After generalizing the matrices of costs and time in a multimodal transportation network, the -shortest path algorithm is applied to providing some potential feasible paths. A multi-objective genetic algorithm is proposed at the lower level to determine the local optimal combination of transportation manners for these potential feasible paths. To make the genetic algorithm more robust, sounder and faster, the Taguchi (orthogonal) experimental design method is adopted in generating the initial population and the crossover operator. The case study shows that the proposed algorithm can effectively find the Pareto-optimal front solutions and offer series of transportation routes with best combinations of transportation manners. The shipper can easily select the required shipping schemes with specified demands.

关键词： multi-objective genetic algorithm Taguchi experimental method multimodal routing Time window

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multi-objective Optimization of RTAB-Map parameters using genetic algorithm for indoor 2D SLAM

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Procedia Computer Science 2024年 250卷 172-181页

作者： Nagamalar Nagarajan Hanxiang Zhang Wei Liu Jason Gu Electrical and Computer Engineering Dalhousie University 1459 Oxford Street Halifax B3H 4R2 Canada Mechanical and Energy Engineering Southern University of Science and Technology 1088 Xueyuan Avenue Shenzhen 518055 China

Currently, Robot Operating System (ROS) provides multiple packages to implement different Simultaneous Localization and Mapping (SLAM) approaches. To effectively obtain sensor data, these packages use parameters whose values are set from prior knowledge and experience with robots and SLAM. In this paper, using a multi-objective genetic algorithm (MOGA) to optimize the values for these parameters is proposed. MOGA allows trade-offs between the objectives using Pareto dominance techniques. Three parameters from the RTAB-Map package are considered for optimization using three different MOGA mechanisms, Dominance Count, Dominance Rank and Switching Fitness. The quality of the map generated for every set of parameters is taken as the indicator of its performance. The number of corners, number of contours and the proportion of occupied cells in the map are used as quantitative measures of map quality. Finally, results obtained from testing the algorithm in simulation are used to test a Quanser QBot2 robot.

关键词： Simultaneous localization and mapping RTAB-Map robot operating system multi-objective genetic algorithm dominance count dominance rank switching fitness

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CBGA-ES⁺: A Cluster-Based genetic algorithm with Non-Dominated Elitist Selection for Supporting multi-objective Test Optimization

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2021年第1期47卷 86-107页

作者： Pradhan, Dipesh Wang, Shuai Ali, Shaukat Yue, Tao Liaaen, Marius Simula Res Lab POB 134 N-1325 Lysaker Norway Univ Oslo Problemveien 7 N-0315 Oslo Norway Testify As POB 134 N-1325 Lysaker Norway Simula Res Lab POB 134 N-1325 Lysaker Norway Cisco Syst Oslo Norway

Many real-world test optimization problems (e.g., test case prioritization) are multi-objective intrinsically and can be tackled using various multi-objective search algorithms (e.g., Non-dominated Sorting genetic algorithm (NSGA-II)). However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In a worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the solutions in the next generation. To address such a challenge, we propose CBGA-ES+, a novel cluster-based genetic algorithm with non-dominated elitist selection to reduce the randomness when selecting the parent solutions to support multi-objective test optimization. We empirically compared CBGA-ES+ with random search and greedy (as baselines), four commonly used multi-objective search algorithms (i.e., multi-objective Cellular genetic algorithm (MOCell), NSGA-II, Pareto Archived Evolution Strategy (PAES), and Strength Pareto Evolutionary algorithm (SPEA2)), and the predecessor of CBGA-ES+ (named CBGA-ES) using five multi-objective test optimization problems with eight subjects (two industrial, one real world, and five open source). The results showed that CBGA-ES+ managed to significantly outperform the selected search algorithms for a majority of the experiments. Moreover, for the solutions in the same search space, CBGA-ES+ managed to perform better than CBGA-ES, MOCell, NSGA-II, PAES, and SPEA2 for 2.2, 13.6, 14.5, 17.4, and 9.9 percent, respectively. Regarding the running time of the algorithm, CBGA-ES+ was faster than CBGA-ES for all the experiments.

关键词： Elitist selection multi-objective genetic algorithm multi-objective test optimization search

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Subpopulation preference adjective non-dominated sorting genetic algorithm for multi-objective capacity expansion for matured fabs

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Kuo, Hsuan-An Peng, Chia-Ching Chien, Chen-Fu Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Natl Tsing Hua Univ AIMS Fellows Execut Master Program Hsinchu 30013 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management 101 Sect 2 Kuang Fu Rd Hsinchu 30013 Taiwan

Capacity expansion strategy consists of fulfilling the demand with capacity portfolio via alternative configurations with long lead time for procurement and installation. Sequential dependent decisions including demand planning and cost structure of capital expenditure shall be taken into consideration for achieving enterprise profitability. Though a number of studies have been done to address related issues, limitations of existing approaches can be traced in part to the lack of a systematic framework in which multiple objectives of related total resource management problems can be considered and integrated. Little research has been done to address the present problem for capacity expansion for matured fabs from the perspective of total resource management. To fill the gaps, this study applies the concept of total resource management to integrate operational strategies and the overall usage of resources. This study develops a capacity expansion model with multiple objectives including minimizing resource costs, maximizing overall return, maximizing revenue, and minimizing capacity risk. Since the formulation of the multi-objectives can be non-linear and the size of the problem is increasing, it is difficult to solve the problem in reasonable time for practical use. A subpopulation preference adjective non-dominated sorting genetic algorithm (SPANS-GA) is developed to solve the decision problem. An empirical study in a leading semiconductor company in Taiwan is conducted for validation. The results have shown practical viability of the developed solution for multi-objective capacity planning for matured wafer fabs for total resource management.

关键词： Capacity expansion Total resource management multi-objective genetic algorithm Matured fabs Semiconductor manufacturing

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multi-objective optimisation of injector and diesel engine by genetic algorithm: Nu-SVR modelling

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INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS 2020年第3期27卷 340-358页

作者： Taghavifar, Hadi Anvari, Simin Malayer Univ Fac Engn Dept Mech Engn Malayer Iran Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian Peoples R China

The current study deals with application of evolutionary multi-objective genetic algorithm (MOGA) in a 1.8 L Ford diesel engine to enhance the power, fuel consumption, and air-fuel uniformity. To do so, four design parameters of engine geometry and injector parameters are defined and three sub-objectives are considered to get either maximised or minimised On the sub-objectives, constraints are imposed to introduce the feasible solutions. The best solution is obtained at RunID66 from 70 design points. The results showed that increasing bowl radius after a certain point is not useful for enhancement of the mixture homogeneity. In the second part of the study, support vector regression (SVR) technique is applied on the input and output data to make a model to predict the engine power.

关键词： diesel engine diesel injector Nu-SVR MOGA multi-objective genetic algorithm Pareto front

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Performance prediction and geometry optimization of ejector in PEMFC system using coupled CFD-BPNN and genetic algorithm

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APPLIED THERMAL ENGINEERING 2024年 251卷

作者： Pang, Zihui Han, Jiquan Feng, Jianmei Diao, Anna Yao, Yanchen Peng, Xueyuan Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Peoples R China Shanghai Marine Diesel Engine Res Inst Shanghai 201108 Peoples R China Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Shaanxi Peoples R China

The ejector is a promising hydrogen recirculation device in proton exchange membrane fuel cell (PEMFC) systems. However, the limited entrainment performance of the ejector at wide operating conditions hinders its development and widespread application in PEMFC systems. To address this challenge and design a highperformance ejector adapted to the wide power range of PEMFC systems, a backpropagation neural network (BPNN) model with computational fluid dynamics (CFD) simulation data is developed. The sensitivity analysis of geometric parameters based on the CFD model reveals that the nozzle throat diameter and the mixing chamber diameter exert the most pronounced impact on the entrainment performance, with average influence rates of 24% and 57%, respectively. Furthermore, two advanced multi-objective genetic algorithms are applied to improve ejector performance. The linear weighted genetic algorithm (LWGA) method proves effective in elevating the overall ejector performance, achieving a remarkable enhancement in entrainment performance of up to 7.9%. On the other hand, the non-dominated sorting genetic algorithm (NSGA- II) method is favorable for expanding the operational power range of the ejector by 30%, as well as a 4.5% increase in overall ejector performance. This work provides a robust framework for designing and optimizing high-performance ejectors in high-power PEMFC systems.

关键词： Ejector multi-objective genetic algorithm Backpropagation neural network PEMFC

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multi-objective genetic algorithm for integrated process planning and scheduling with fuzzy processing time

Multi-objective genetic algorithm for integrated process pla...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Wen, Xiaoyu Li, Xinyu Gao, Liang Wan, Liang Wang, Wenwen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781467363433

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multiobjective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm fuzzy processing time

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