English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this study, a novel multi-objective reliability-based design optimization (MORBDO) method considering the maximum allowable deviation range of design variables is proposed for the reducer housing of electric vehicles. First, the numerical model of the reducer housing is established by ABAQUS and verified by experiments. A radial basis function (RBF) neural network model is used to construct the approximate finite element model. The structural parameters of the RBF are optimized using the heuristic global optimization ability of the particle swarm optimization (PSO) algorithm. Sequential quadratic programming (SQP) and non-dominated sorting genetic algorithm II (NSGA II) are used to perform the MORBDO. Finally, the technique for order preference by similarity to ideal solution, a multi-criteria decision-making (MCDM) method, is used to select the ideal design in multi-objective Pareto points. The optimization method generated a set of Pareto non-dominated solutions with three objectives, which can be selected for a more feasible scheme using MCDM. The proposed method comprehensively measures the requirements of manufacturing and performance criteria, and the optimization results provide a variety of optimization design schemes for the reducer housing of electric vehicles.

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