scholarly journals Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6236
Author(s):  
Jinhwan Park ◽  
Donghyeon Yoo ◽  
Jaemin Moon ◽  
Janghyeok Yoon ◽  
Jungtae Park ◽  
...  
Keyword(s):  
Energy Density ◽  
Design Optimization ◽  
Failure Rate ◽  
Robust Design ◽  
Lithium Ion ◽  
High Energy ◽  
Robust Design Optimization ◽  
Design Variables ◽  
Battery Capacity ◽  
Reliability And Robustness

Lithium-ion batteries (LIBs) are increasingly employed in electric vehicles (EVs) owing to their advantages, such as low weight, and high energy and power densities. However, the uncertainty encountered in the manufacturing of LIB cells increases the failure rate and causes cell-to-cell variations, thereby degrading the battery capacity and lifetime. In this study, the reliability and robustness of LIB cells were improved using the design of experiments (DOE), and the reliability-based robust design optimization (RBRDO) approaches. First, design factors sensitive to the energy density and power density were selected as design variables through sensitivity analysis using the DOE. RBRDO was performed to maximize the energy density while reducing the failure rate and cell-to-cell variations. To verify the superiority of the reliability and robustness offered by RBRDO, the obtained results were compared with those from conventional deterministic design optimization (DDO), and reliability-based design optimization (RBDO). RBRDO increased the mean of the energy density by 33.5% compared to the initial value and reduced the failure rate by 98.9%, due to improved reliability, compared to DDO. Moreover, RBRDO reduced the standard deviation in the energy density (i.e., cell-to-cell variations) by 30.0% due to the improved robustness compared to RBDO.

scholarly journals Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shujuan Wang ◽  
Qiuyang Li ◽  
Gordon J. Savage
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Robust Design ◽  
Structural Reliability ◽  
Computational Cost ◽  
Optimization Process ◽  
Robust Design Optimization ◽  
Global Approximation ◽  
Design Variables ◽  
Uncertainty In Design

This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO) method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2003 ◽  
Vol 125 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2001 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

Abstract In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed approach, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Alternative Methods for Reliability-Based Robust Design Optimization Including Dimension Reduction Method

2006 ◽  
Author(s):  
Ikjin Lee ◽  
Kyung K. Choi ◽  
Liu Du
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Reduction Method ◽  
Statistical Moments ◽  
Robust Design Optimization ◽  
Quality Loss ◽  
Dimension Reduction Method ◽  
Quality Loss Function ◽  
Performance Function ◽  
Design Variables

The objective of reliability-based robust design optimization (RBRDO) is to minimize the product quality loss function subject to probabilistic constraints. Since the quality loss function is usually expressed in terms of the first two statistical moments, mean and variance, many methods have been proposed to accurately and efficiently estimate the moments. Among the methods, the univariate dimension reduction method (DRM), performance moment integration (PMI), and percentile difference method (PDM) are recently proposed methods. In this paper, estimation of statistical moments and their sensitivities are carried out using DRM and compared with results obtained using PMI and PDM. In addition, PMI and DRM are also compared in terms of how accurately and efficiently they estimate the statistical moments and their sensitivities of a performance function. In this comparison, PDM is excluded since PDM could not even accurately estimate the statistical moments of the performance function. Also, robust design optimization using DRM is developed and then compared with the results of RBRDO using PMI and PDM. Several numerical examples are used for the two comparisons. The comparisons show that DRM is efficient when the number of design variables is small and PMI is efficient when the number of design variables is relatively large. For the inverse reliability analysis of reliability-based design, the enriched performance measure approach (PMA+) is used.

scholarly journals Robust Design Optimization with Penalty Function for Electric Oil Pumps with BLDC Motors

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 153 ◽  
Author(s):  
Keun-Young Yoon ◽  
Soo-Whang Baek
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Robust Design ◽  
Penalty Function ◽  
Block Diagram ◽  
Robust Design Optimization ◽  
Bldc Motor ◽  
Control Block ◽  
Element Analysis ◽  
Design Variables

In this paper, we propose and evaluate a robust design optimization (RDO) algorithm for the shape of a brushless DC (BLDC) motor used in an electric oil pump (EOP). The components of the EOP system and the control block diagram for driving the BLDC motor are described. Although the conventional deterministic design optimization (DDO) method derives an appropriate combination of design goals and target performance, DDO does not allow free searching of the entire design space because it is confined to preset experimental combinations of parameter levels. To solve this problem, we propose an efficient RDO method that improves the torque characteristics of BLDC motors by considering design variable uncertainties. The dimensions of the stator and the rotor were selected as the design variables for the optimal design and a penalty function was applied to address the disadvantages of the conventional Taguchi method. The optimal design results obtained through the proposed RDO algorithm were confirmed by finite element analysis, and the improvement in torque and output performance was confirmed through experimental dynamometer tests of a BLDC motor fabricated according to the optimization results.

Reliability-Based Robust Design Optimization in Consideration of Manufacturing Tolerance by Multi-Objective Evolutionary Optimization with Repair Algorithm

2021 ◽  
pp. 2150005
Author(s):  
Gang Li ◽  
Ye Liu ◽  
Gang Zhao ◽  
Yan Zeng
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Robust Design Optimization ◽  
Manufacturing Cost ◽  
Multi Objective Optimization ◽  
Random Design ◽  
Multi Objective ◽  
Design Variables ◽  
Objective Robustness ◽  
Repair Operator

There are inherently various uncertainties in practical engineering, and reliability-based design optimization (RBDO) and robust design optimization (RDO) are two well-established methodologies when considering the uncertainties. Naturally, reliability-based robust design optimization (RBRDO) is a methodology developed recently by combining RBDO and RDO, in which the tolerances of random design variables are always assumed as constants. However, the tolerance of random design variables is a key factor for the objective robustness and manufacturing cost, and the tolerance allocation is the core problem in mechanical manufacturing. Inspired by the cost–tolerance relationship in mechanical manufacturing, this paper presents an integrated framework to simultaneously find the optimal design variable and the corresponding tolerance in the multi-objective RBRDO, with the trade-off between objective robustness and manufacturing cost. The failure mechanism of the constraint handling strategy of the constrained reference vector-guided evolutionary algorithm (C-RVEA) is discussed to solve the multi-objective optimization formulation. Then the robust repair operator and reliability-based repair operator are proposed to transform unfeasible solutions to the feasible ones under reliability constraints. Numerical results reveal that the proposed repair algorithm is effective. By solving the integrated multi-objective optimization problem, the Pareto front with the compromised solutions between the objective robustness and manufacturing cost could be obtained, from which decision makers can select the satisfying solution conveniently according to the preferred requirements.

A Sequential Algorithm for Reliability-Based Robust Design Optimization Under Epistemic Uncertainty

2012 ◽  
Vol 134 (1) ◽  
Author(s):  
Yuanfu Tang ◽  
Jianqiao Chen ◽  
Junhong Wei
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Epistemic Uncertainty ◽  
Robust Design Optimization ◽  
Probabilistic Constraints ◽  
Sequential Algorithm ◽  
Practical Applications ◽  
Reliability Based Design ◽  
Design Variables ◽  
Novel Concept

In practical applications, there may exist a disparity between real values and optimal results due to uncertainties. This kind of disparity may cause violations of some probabilistic constraints in a reliability based design optimization (RBDO) problem. It is important to ensure that the probabilistic constraints at the optimum in a RBDO problem are insensitive to the variations of design variables. In this paper, we propose a novel concept and procedure for reliability based robust design in the context of random uncertainty and epistemic uncertainty. The epistemic uncertainty of design variables is first described by an info gap model, and then the reliability-based robust design optimization (RBRDO) is formulated. To reduce the computational burden in solving RBRDO problems, a sequential algorithm using shifting factors is developed. The algorithm consists of a sequence of cycles and each cycle contains a deterministic optimization followed by an inverse robustness and reliability evaluation. The optimal result based on the proposed model satisfies certain reliability requirement and has the feasible robustness to the epistemic uncertainty of design variables. Two examples are presented to demonstrate the feasibility and efficiency of the proposed method.

A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design

2018 ◽  
Vol 233 (3) ◽  
pp. 918-936
Author(s):  
Mohsen Bidoki ◽  
Mehdi Mortazavi ◽  
Mehdi Sabzehparvar
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Robust Design ◽  
Multidisciplinary Design Optimization ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Robust Design Optimization ◽  
Multidisciplinary Design ◽  
Optimization Framework ◽  
Design Variables

The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines.

Simulation-based robust design of complex product considering uncertainties of metamodel, design variables, and noise parameters

2016 ◽  
Vol 231 (20) ◽  
pp. 3715-3727 ◽  
Author(s):  
Zhenyu Liu ◽  
Xiang Peng ◽  
Chan Qiu ◽  
Jianrong Tan ◽  
Guifang Duan ◽  
...  
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Product Performance ◽  
Robust Design Optimization ◽  
Complex Product ◽  
Noise Parameters ◽  
Design Variables ◽  
Simulation Based ◽  
Metamodel Uncertainty ◽  
Performance Uncertainty

The uncertainties of design variables, noise parameters, and metamodel are important factors in simulation-based robust design optimization. Most conventional metamodel construction methods only consider one or two uncertainties. In this paper, a new surrogate modeling method simultaneously measuring all the uncertainties is proposed for simulation-based robust design optimization of complex product. The effect of metamodel uncertainty on product performance uncertainty is quantified through uncertainty propagation analysis among design variables uncertainty, noise parameters uncertainty, metamodel uncertainty, and performance uncertainty. Then, the sampling points are selected and the metamodel is constructed based on the predictive interval of product performance and mean square error of the Kriging metamodel. The constructed metamodel is applied to robust design optimization considering multiple uncertainties. Results of two mathematical examples show that the proposed metamodel considering multiple uncertainties increases the result accuracy of robust design optimization. Finally, the proposed algorithm is applied to robust design optimization of a heat exchanger, and the total heat transfer rate is enhanced under uncertainties of fin structural parameters, operation conditions parameters and simulation metamodel.

scholarly journals Robust Design Optimization of an Aerospace Vehicle Prolusion System

2011 ◽  
Vol 2011 ◽  
pp. 1-18
Author(s):  
Muhammad Aamir Raza ◽  
Wang Liang
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Orthogonal Design ◽  
Pattern Search ◽  
Robust Design Optimization ◽  
Performance Parameters ◽  
Worst Case ◽  
Design Variables ◽  
Hybrid Simulated Annealing ◽  
Optimization Methodology

This paper proposes a robust design optimization methodology under design uncertainties of an aerospace vehicle propulsion system. The approach consists of 3D geometric design coupled with complex internal ballistics, hybrid optimization, worst-case deviation, and efficient statistical approach. The uncertainties are propagated through worst-case deviation using first-order orthogonal design matrices. The robustness assessment is measured using the framework of mean-variance and percentile difference approach. A parametric sensitivity analysis is carried out to analyze the effects of design variables variation on performance parameters. A hybrid simulated annealing and pattern search approach is used as an optimizer. The results show the objective function of optimizing the mean performance and minimizing the variation of performance parameters in terms of thrust ratio and total impulse could be achieved while adhering to the system constraints.

Sign in / Sign up

Export Citation Format

Share Document