Open Access

ARTICLE

Multi-Objective Optimization of Aluminum Alloy Electric Bus Frame Connectors for Enhanced Durability

by Wenjun Zhou^1,2, Mingzhi Yang¹, Qian Peng², Yong Peng^1,*, Kui Wang¹, Qiang Xiao¹

1 Key Laboratory of Traffic Safety on Track, Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410018, China
2 School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, China

* Corresponding Author: Yong Peng. Email:

(This article belongs to the Special Issue: Structural Design and Optimization)

Computer Modeling in Engineering & Sciences 2024, 140(1), 735-755. https://doi.org/10.32604/cmes.2024.047258

Received 31 October 2023; Accepted 18 January 2024; Issue published 16 April 2024

Abstract

The widespread adoption of aluminum alloy electric buses, known for their energy efficiency and eco-friendliness, faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel. This issue is further exacerbated by the stringent requirements imposed by the flammability and explosiveness of batteries, necessitating robust frame protection. Our study aims to optimize the connectors of aluminum alloy bus frames, emphasizing durability, energy efficiency, and safety. This research delves into Multi-Objective Coordinated Optimization (MCO) techniques for lightweight design in aluminum alloy bus body connectors. Our goal is to enhance lightweighting, reinforce energy absorption, and improve deformation resistance in connector components. Three typical aluminum alloy connectors were selected and a design optimization platform was built for their MCO using a variety of software and methods. Firstly, through three-point bending experiments and finite element analysis on three types of connector components, we identified optimized design parameters based on deformation patterns. Then, employing Optimal Latin hypercube design (OLHD), parametric modeling, and neural network approximation, we developed high-precision approximate models for the design parameters of each connector component, targeting energy absorption, mass, and logarithmic strain. Lastly, utilizing the Archive-based Micro Genetic Algorithm (AMGA), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm (NSGA2), we explored optimized design solutions for these joint components. Subsequently, we simulated joint assembly buckling during bus rollover crash scenarios to verify and analyze the optimized solutions in three-point bending simulations. Each joint component showcased a remarkable 30%–40% mass reduction while boosting energy absorption. Our design optimization method exhibits high efficiency and cost-effectiveness. Leveraging contemporary automation technology, the design optimization platform developed in this study is poised to facilitate intelligent optimization of lightweight metal components in future applications.

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