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

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.

Control and Design Optimization of a Novel Hybrid Electric Powertrain System

2017 ◽  
Author(s):  
Chenyu Yi ◽  
Bogdan Epureanu
Keyword(s):  
Optimal Control ◽  
Design Optimization ◽  
Fuel Economy ◽  
Optimization Problems ◽  
Computational Cost ◽  
Electric Machines ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
And Control ◽  
Electric Powertrains

Control and design optimization of hybrid electric powertrains is necessary to maximize the benefits of novel architectures. Previous studies have proposed multiple optimal and near-optimal control methods, approaches for design optimization, and ways to solve coupled design and control optimization problems for hybrid electric powertrains. This study presents control and design optimization of a novel hybrid electric powertrain architecture to evaluate its performance and potential using physics-based models for the electric machines, the battery and a near-optimal control, namely the equivalent consumption minimization strategy. Design optimization in this paper refers to optimizing the sizes of the powertrain components, i.e. electric machines, battery and final drive. The control and design optimization problem is formulated using nested approach with sequential quadratic programming as design optimization method. Metamodeling is applied to abstract the near-optimal powertrain control model to reduce the computational cost. Fuel economy, sizes of components, and consistency of city and highway fuel economy are reported to evaluate the performance of the powertrain designs. The results suggest an optimal powertrain design and control that grants good performance. The optimal design is shown to be robust and non-sensitive to slight component size changes when evaluated for the near-optimal control.

Hybrid (Symbolic-Numerical) Optimization in Mechanism Design for the Elimination of Redundant Constraints

1991 ◽  
Vol 113 (2) ◽  
pp. 110-123 ◽  
Author(s):  
D. A. Hoeltzel ◽  
Wei-Hua Chieng
Keyword(s):  
Design Optimization ◽  
Numerical Optimization ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Systematic Approach ◽  
Hybrid Design ◽  
New Approach ◽  
Symbolic Reasoning ◽  
Algorithmic Analysis ◽  
Programming Algorithms

Hybrid optimization, a new approach to design optimization employing both symbolic reasoning and algorithmic analysis, has been applied to the design of kinematic pairs in mechanisms. This hybrid design methodology provides a three-step systematic approach for (1) combining the degrees-of-freedom found in simple, lower kinematic pairs to obtain more complex but robust higher pairs, (2) judging inappropriately assigned joints for the elimination of redundant kinematic constraints and harmful mobilities, and (3) assisting nonexpert designers in applying nonlinear programming algorithms for detailed numerical design optimization of kinematic pairs. An example taken from the design of a spatial mechanism, specifically a universal joint, is presented and serves to demonstrate the utility of this procedure for detailed hybrid design optimization of kinematic pairs in mechanisms.

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.

Driving Torsion Scans with Wavefront Propagation

2020 ◽  
Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang
Keyword(s):  
Potential Energy ◽  
Force Field ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Initial Guess ◽  
Field Development ◽  
Force Field Development ◽  
Wavefront Propagation ◽  
Open Source Software Package

<div>The parameterization of torsional / dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields.</div><div>Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms.</div><div>To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values.</div><div>However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses.</div><div>In this paper we propose a systematic and versatile workflow called \textit{TorsionDrive} to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development.</div><div>The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described.</div><div>The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.</div>

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

A two-stage design optimization framework for multifield origami-inspired structures

2021 ◽  
pp. 1045389X2110116
Author(s):  
Wei Zhang ◽  
Saad Ahmed ◽  
Jonathan Hong ◽  
Zoubeida Ounaies ◽  
Mary Frecker
Keyword(s):  
Case Studies ◽  
Computing Time ◽  
Computational Cost ◽  
High Fidelity ◽  
Fe Model ◽  
Two Stage ◽  
Baseline Design ◽  
Optimization Framework ◽  
Highly Nonlinear ◽  
Stage 1

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

Momentum exchange impact damper design methodology for object-wall-collision problems

2017 ◽  
Vol 24 (14) ◽  
pp. 3206-3218
Author(s):  
Yohei Kushida ◽  
Hiroaki Umehara ◽  
Susumu Hara ◽  
Keisuke Yamada
Keyword(s):  
Optimal Design ◽  
Design Methodology ◽  
Design Parameters ◽  
Momentum Exchange ◽  
Impact Damper ◽  
One Dimensional ◽  
Practical Design ◽  
Wall Collision ◽  
Damper Design ◽  
And Control

Momentum exchange impact dampers (MEIDs) were proposed to control the shock responses of mechanical structures. They were applied to reduce floor shock vibrations and control lunar/planetary exploration spacecraft landings. MEIDs are required to control an object’s velocity and displacement, especially for applications involving spacecraft landing. Previous studies verified numerous MEID performances through various types of simulations and experiments. However, previous studies discussing the optimal design methodology for MEIDs are limited. This study explicitly derived the optimal design parameters of MEIDs, which control the controlled object’s displacement and velocity to zero in one-dimensional motion. In addition, the study derived sub-optimal design parameters to control the controlled object’s velocity within a reasonable approximation to derive a practical design methodology for MEIDs. The derived sub-optimal design methodology could also be applied to MEIDs in two-dimensional motion. Furthermore, simulations conducted in the study verified the performances of MEIDs with optimal/sub-optimal design parameters.

Analysis and Optimization of a New Differentially Driven Cable Parallel Robot

2015 ◽  
Vol 7 (3) ◽  
Author(s):  
Hamed Khakpour ◽  
Lionel Birglen ◽  
Souheil-Antoine Tahan
Keyword(s):  
Kinematic Analysis ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Parallel Robot ◽  
Cable Robot ◽  
Proper Design ◽  
Two Indices ◽  
Three Degrees Of Freedom ◽  
Cable Robots

In this paper, a new three degrees of freedom (DOF) differentially actuated cable parallel robot is proposed. This mechanism is driven by a prismatic actuator and three cable differentials. Through this design, the idea of using differentials in the structure of a spatial cable robot is investigated. Considering their particular properties, the kinematic analysis of the robot is presented. Then, two indices are defined to evaluate the workspaces of the robot. Using these indices, the robot is subsequently optimized. Finally, the performance of the optimized differentially driven robot is compared with fully actuated mechanisms. The results show that through a proper design methodology, the robot can have a larger workspace and better performance using differentials than the fully driven cable robots using the same number of actuators.

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