scholarly journals Recent Design Optimization Methods for Energy-Efficient Electric Motors and Derived Requirements for a New Improved Method—Part 1

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1400
Author(s):  
Johannes Schmelcher ◽  
Max Kleine Büning ◽  
Kai Kreisköther ◽  
Dieter Gerling ◽  
Achim Kampker
Keyword(s):  
Rare Earth ◽  
Design Optimization ◽  
Energy Efficient ◽  
Degrees Of Freedom ◽  
High Efficiency ◽  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Electric Motors ◽  
Electric Cars

Energy-efficient electric motors are gathering an increased attention since they are used in electric cars or to reduce operational costs, for instance. Due to their high efficiency, permanent-magnet synchronous motors are used progressively more. However, the need to use rare-earth magnets for such high-efficiency motors is problematic not only in regard to the cost but also in socio-political and environmental aspects. Therefore, an increasing effort has to be put in finding the best design possible. The goals to achieve are, among others, to reduce the amount of rare-earth magnet material but also to increase the efficiency. In the first part of this multipart paper, characteristics of optimization problems in engineering and general methods to solve them are presented. In part two, different approaches to the design optimization problem of electric motors are highlighted. The last part will evaluate the different categories of optimization methods with respect to the criteria: degrees of freedom, computing time and the required user experience. As will be seen, there is a conflict of objectives regarding the criteria mentioned above. Requirements, which a new optimization method has to fulfil in order to solve the conflict of objectives will be presented in this last paper.

scholarly journals Recent Design Optimization Methods for Energy-Efficient Electric Motors and Derived Requirements for a New Improved Method—Part 3

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1402
Author(s):  
Johannes Schmelcher ◽  
Max Kleine Büning ◽  
Kai Kreisköther ◽  
Dieter Gerling ◽  
Achim Kampker
Keyword(s):  
Design Optimization ◽  
Energy Efficient ◽  
Degrees Of Freedom ◽  
Computing Time ◽  
Optimization Methods ◽  
Optimization Method ◽  
Complex Problem ◽  
Electric Motors ◽  
Improved Method ◽  
Previous Part

The design of energy-efficient electric motor is a complex problem since diverse requirements and competing goals have to be fulfilled simultaneously. Therefore, different approaches to the design optimization of electric motors have been developed, each of them has its own advantages and drawbacks. The characteristics of these approaches were presented in the previous part of this multipart paper. In this paper, the presented approaches will be assessed with respect to the criteria: degrees of freedom, computing time and the required user experience. A conflict of objectives will become apparent. Based on these findings, requirements for a new design optimization method with the aim to solve the conflict of objectives, will be formulated.

scholarly journals Recent Design Optimization Methods for Energy-Efficient Electric Motors and Derived Requirements for a New Improved Method—Part 2

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1401
Author(s):  
Johannes Schmelcher ◽  
Max Kleine Büning ◽  
Kai Kreisköther ◽  
Dieter Gerling ◽  
Achim Kampker
Keyword(s):  
Design Optimization ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Electric Motors ◽  
Multiple Goals ◽  
Improved Method ◽  
Different Types ◽  
Solution Methods

Designing energy-efficient electric motor is a task where multiple goals have to be achieved at once. To find the best design possible, different approaches have been developed. In part one of this multipart paper, the characteristics of the design optimization problem and methods to solve them have been presented. Part two will deal with the different types of model descriptions and how the fundamental workflows look like. The third and last paper will evaluate the findings concerning the solution methods of the design optimization problem of electric motors. As a consequence, requirements for a new improved optimization method are deduced and presented.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Development of a Common Platform for Testing Metamodel Based Design Optimization Methods

2013 ◽  
Author(s):  
Adel A. Younis ◽  
George H. Cheng ◽  
G. Gary Wang ◽  
Zuomin Dong
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Computation Time ◽  
Optimization Methods ◽  
Global Optimum ◽  
Test Problems ◽  
Test Bed ◽  
Test Procedures ◽  
Local Optima

Metamodel based design optimization (MBDO) algorithms have attracted considerable interests in recent years due to their special capability in dealing with complex optimization problems with computationally expensive objective and constraint functions and local optima. Conventional unimodal-based optimization algorithms and stochastic global optimization algorithms either miss the global optimum frequently or require unacceptable computation time. In this work, a generic testbed/platform for evaluating various MBDO algorithms has been introduced. The purpose of the platform is to facilitate quantitative comparison of different MBDO algorithms using standard test problems, test procedures, and test outputs, as well as to improve the efficiency of new algorithm testing and improvement. The platform consists of a comprehensive test function database that contains about 100 benchmark functions and engineering problems. The testbed accepts any optimization algorithm to be tested, and only requires minor modifications to meet the test-bed requirements. The testbed is useful in comparing the performance of competing algorithms through execution of same problems. It allows researchers and practitioners to test and choose the most suitable optimization tool for their specific needs. It also helps to increase confidence and reliability of the newly developed MBDO tools. Many new MBDO algorithms, including Mode Pursuing Sampling (MPS), Pareto Set Pursuing (PSP), and Space Exploration and Unimodal Region Elimination (SEUMRE), were tested in this work to demonstrate its functionality and benefits.

A fast genetic algorithm for solving architectural design optimization problems

2015 ◽  
Vol 29 (4) ◽  
pp. 457-469 ◽  
Author(s):  
Zhouzhou Su ◽  
Wei Yan
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Domain Knowledge ◽  
Architectural Design ◽  
Optimization Problems ◽  
Computing Time ◽  
Divide And Conquer ◽  
Specific Domain ◽  
Building Performance Simulation ◽  
Simulation And Optimization

AbstractBuilding performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

Reliability-based design optimization for vertical vibrations of a modified electric vehicle using fourth-moment polynomial standard transformation method

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2649-2664
Author(s):  
Sheng Wang ◽  
Lin Hua ◽  
Xinghui Han ◽  
Zhuoyu Su
Keyword(s):  
Design Optimization ◽  
Electric Vehicle ◽  
Optimization Problems ◽  
Computing Time ◽  
Optimization Procedure ◽  
Transformation Method ◽  
Vertical Vibration ◽  
Fourth Moment ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design

This article presents a new reliability-based design optimization procedure for the vertical vibration issues raised by a modified electric vehicle using fourth-moment polynomial standard transformation method. First, the fourth-moment polynomial standard transformation method with polynomial chaos expansion is used to obtain the reliability index of uncertain constraints in the reliability-based design optimization which is highly precise and saves computing time compared with other common methods. Next, the half-car model with nonlinear suspension parameters for the modified electric vehicle is investigated, and the response surface methodology is adopted to approximate the complex and time-consuming vertical vibration calculation to the polynomial expressions, and the approximation is validated for reliability-based design optimization results within permissible error level. Then, reliability-based design optimization results under both deterministic and uncertain load parameters are shown and analyzed. Unlike the traditional vertical vibration optimization that only considers one or several sets of load parameters, which lacks versatility, this article presents the reliability-based design optimization with uncertain load parameters which is more suitable for engineering. The results show that the proposed reliability-based design optimization procedure is an effective and efficient way to solve vertical vibration optimization problems for the modified electric vehicle, and the optimization statistics, including the maximum probability interval, can provide references for other suspension dynamical optimization.

Mono and bi-Level optimization architectures for powertrain design

2016 ◽  
Vol 35 (3) ◽  
Author(s):  
Pierre Caillard ◽  
Frederic Gillon ◽  
Sid-Ali Randi ◽  
Noelle Janiaud
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Computing Time ◽  
Computational Cost ◽  
New Guidelines ◽  
Electric Machine Control ◽  
Optimization Loop ◽  
Electric Powertrains

Purpose The purpose of this paper is to compare two design optimization architectures for the optimal design of a complex device that integrates simultaneously the sizing of system components and the control strategy for increasing the energetic performances. The considered benchmark is a battery electric passenger car. Design/methodology/approach The optimal design of an electric vehicle powertrain is addressed within this paper, with regards to performances and range. The objectives and constraints require simulating several vehicle operating points, each of them has one degree of freedom for the electric machine control. This control is usually determined separately for each point with a sampling or an optimization loop resulting in an architecture called bi-level. In some conditions, the control variables can be transferred to the design optimization loop by suppressing the inner loop to get a mono-level formulation. The paper describes in which conditions this transformation can be done and compares the results for both architectures. Findings Results show a calculation time divided by more than 30 for the mono-level architecture compared to the natural bi-level on the study case. Even with the same models and optimization algorithms, the structure of the problem should be studied to improve the results, especially if computational cost is high. Originality/value The compared architectures bring new guidelines in the field optimal design for electric powertrains. The way to formulate a design optimization with some inner degrees of freedom can have a significant impact on computing time and on the problem understanding

Formulation of Multicriterion Design Optimization Problems for Solution With Scalar Numerical Optimization Methods

2004 ◽  
Vol 48 (01) ◽  
pp. 61-76 ◽  
Author(s):  
Michael G. Parsons ◽  
Randall L. Scott
Keyword(s):  
Design Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Pareto Optimum ◽  
Compromise Solution ◽  
Design Problems ◽  
Common Definition ◽  
Bulk Carriers ◽  
Definition Of

Most marine design problems involve multiple conflicting criteria, objectives, or goals. The most common definition of the multicriterion optimum is the Pareto optimum, which usually results in a set of solutions. Design teams, however, need to arrive at a single answer that provides an acceptable compromise solution within the Pareto set. Methods have been developed to solve multicriterion optimization problems using a number of related definitions of the compromise solution or "optimum" in the presence of multiple conflicting criteria. The most common of these definitions are reviewed and their solutions are formulated in a consistent form utilizing a preference function that will allow their solution using conventional scalar criterion numerical optimization methods. This approach permits the use and comparison of the various definitions of the multicriterion "optimum" with modest additional computation. The design team can use these results to guide its selection of the solution that best reflects their design intent in a particular case. A sixparameter, three-criterion, 14-to 16-constraint conceptual marine design optimization example adapted from the literature is presented to illustrate the use of this approach. The results for the various definitions of the multicriterion optimum for Panamax and post-Panamax bulk carriers are presented for comparison.

Optimization of Machine System Designs Using Hierarchical Decomposition Based on Criteria Influence

2002 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui ◽  
Shigeaki Komori
Keyword(s):  
Design Optimization ◽  
Structural Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Hierarchical Level ◽  
Hierarchical Optimization ◽  
Pareto Optimum ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Basic Characteristics

Machine product designs routinely have so many mutually related characteristics that common design optimization methods often result in an unsatisfactory local optimum solution. In order to overcome this problem, this paper proposes a design optimization method based on the clarification of the conflicting and cooperative relationships among the characteristics. First of all, each performance characteristic is divided into simpler basic characteristics according to its structure. Next, the relationships among the basic characteristics are systematically identified and clarified. Then, based on this clarification, the optimization problem is expressed using hierarchical constructions of these basic characteristics and design variables related to the most basic characteristics. Finally, an optimization strategy and detailed hierarchical optimization procedures are constructed, after clarifying the influence levels of each basic characteristic upon the objective functions and setting a core characteristic for the product under consideration. Here, optimizations are sequentially repeated starting with the basic optimal unit group at the bottom hierarchical level and proceeding to higher levels by the hierarchical genetic algorithms. Then, the Pareto optimum solutions at the top hierarchical level are obtained. With the proposed optimization methods, optimization can be more easily applied after the optimization problems have been simplified by decomposition. In doing so, the volume of design spaces for each optimization is reduced, while useful and unique rules and laws may be uncovered. The optimization strategy expressed by the hierarchical structures can be used for the optimization of similar product designs, which realize these breakthroughs, yielding improved product performances. The proposed method is applied to a machine-tool structural model.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.

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