Multi-objective reliability-based design optimization for the reducer housing of electric vehicles

Xu, Xipeng; Chen, Xinbo; Liu, Zhe; Yang, Jun; Xu, Yanan; Zhang, Yong; Gao, Yunkai

doi:10.1080/0305215x.2021.1923704

Cited by 22 publications

(11 citation statements)

References 27 publications

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“…Typically, different Multi-Criteria Decision-Making (MCDM) methods (which refer to problems with no explicit objective functions, and rather comprising a decision matrix with a list of alternatives/options along with pre-defined criteria values) are applied to find the best solution from Pareto-optimal sets of MOO according to the preferences of decisionmakers (DMs) [20,[22][23][24][25][26][27]. MCDM, in essence, is a management and decision-making support approach which involves multiple alternatives and criteria/objectives [27,28]. In this case, different solutions in the Pareto set become different alternatives in the MCDM problem and the objective functions' values become criteria values.…”

Section: How Multi-criteria Decision-making (Mcdm) Methods Can Be Lin...mentioning

confidence: 99%

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Ferdous,

Bensebaa,

Milani

et al. 2024

Sustainability

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The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there has been a growing interest in hybrid approaches that combine MCDM with MOO, aiming to enhance the efficacy of the final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. To address this gap, this systematic review is designed to develop a generic decision tree that serves as a practical roadmap for practitioners seeking to perform MOO and MCDM in an integrated fashion, with a specific focus on accounting for uncertainties. The systematic review identified the recent studies that conducted both MOO and MCDM in an integrated way. It is important to note that this review does not aim to identify the superior MOO or MCDM methods, but rather it delves into the strategies for integrating these two common methodologies. The prevalent MOO methods used in the reviewed articles were evolution-based metaheuristic methods. TOPSIS and PROMETHEE II are the prevalent MCDM ranking methods. The integration of MOO and MCDM methods can occur either a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. The developed decision tree illustrated all three paths and integrated uncertainty considerations in each path. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application in identifying the optimized processing pathways for sustainably obtaining pulse protein. This study will help practitioners in different research domains use MOO and MCDM methods in an integrated way to identify the most sustainable and optimized system.

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Section: How Multi-criteria Decision-making (Mcdm) Methods Can Be Lin...mentioning

confidence: 99%

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Ferdous,

Bensebaa,

Milani

et al. 2024

Sustainability

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“…Das et al [38] compared the performance of EVs using the evaluation of mixed data. Conversely, the TOPSIS method was used for the reducer housing of EVs [6]. The Fuzzy logic and AHP methods were integrated to evaluate the sustainable location selection of EV fast-charging stations [39].…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to a European Commission document [5], at least 30 million zero-emission vehicles will be in operation on European roads and 100 European cities will be climate neutral by 2030. Therefore, many countries around the world have been trying to adopt and establish the appropriate policies for EVs [6]. With the continuous improvement of battery technology, EVs have become the center of the drive for new energy automobiles.…”

Section: Introductionmentioning

confidence: 99%

An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)—A Case Study of the Electric Vehicle Selection Problem

et al. 2023

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Decision-making is a ubiquitous and paramount issue in the modern business world. Inappropriate decisions may lead to severe consequences for companies. Considering that the evaluation of alternatives is generally affected by several criteria, decision-making should be considered a very challenging task. From the 1980s to the present day, various multi-criteria decision-making (MCDM) methods have evolved, supporting people in the decision-making process. The main aim of this paper is to propose an original MCDM method and to demonstrate its applicability in an empirical case study. To solve the electric vehicle selection problem for the last-mile delivery, we developed and applied a new MCDM method -the AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization) method. To demonstrate the robustness of the proposed method, a comparative analysis with other state-of-the-art MCDM methods is conduct-ed. The results indicate a high level of confidence in the AROMAN method in the decision-making field. In addition, the sensitivity analysis is performed, and the results indicate a high level of stability.

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“…At present, the optimization design method of planetary gear trains mainly considers the determining system parameters and implements the conventional deterministic optimization method. However, for practical engineering structures, many uncertainties are observed in the material properties, manufacturing, and measurement [8][9][10][11][12]. To obtain a reliable structural design, the uncertainties of the planetary gear train of electric vehicles need to be considered.…”

Section: Introductionmentioning

confidence: 99%

Optimization Design for the Planetary Gear Train of an Electric Vehicle under Uncertainties

Chen

Lin

et al. 2022

Actuators

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The planetary gear train is often used as the main device for decelerating and increasing the torque of the drive motor of electric vehicles. Considering the lightweight requirement and existing uncertainty in structural design, a multi-objective uncertainty optimization design (MUOD) framework is developed for the planetary gear train of the electric vehicle in this study. The volume and transmission efficiency of the planetary gear train are taken into consideration as optimization objectives. The manufacturing size, material, and load input of the planetary gear train are considered as uncertainties. An approximate direct decoupling model, based on subinterval Taylor expansion, is applied to evaluate the propagation of uncertainties. To improve the convergence ability of the multi-objective evolutionary algorithm, the improved non-dominated sorting genetic algorithm II (NSGA-II) is designed by using chaotic and adaptive strategies. The improved NSGA-II has better convergence efficiency than classical NSGA-II and multi-objective particle swarm optimization (MOPSO). In addition, the multi-criteria decision making (MCDM) method is applied to choose the most satisfactory solution in Pareto sets from the multi-objective evolutionary algorithm. Compared with the multi-objective deterministic optimization design (MDOD), the proposed MUOD framework has better reliability than MDOD under different uncertainty cases. This MUOD method enables further guidance pertaining to the uncertainty optimization design of transportation equipment, containing gear reduction mechanisms, in order to reduce the failure risk.

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Multi-objective reliability-based design optimization for the reducer housing of electric vehicles

Cited by 22 publications

References 27 publications

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)—A Case Study of the Electric Vehicle Selection Problem

Optimization Design for the Planetary Gear Train of an Electric Vehicle under Uncertainties

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