Harmonizing Aerodynamic Efficiency, Stability, and Acoustic Excellence: Multi-Objective Optimization for Electric Vehicle Rear-End Design

Author:

Beigmoradi Sajjad1

Affiliation:

1. AVL Powertrain UK

Abstract

Abstract

In recent years, the automotive field has exposed a significant surge in electrification, presenting a pivotal solution to environmental concerns. However, addressing the challenge of driving mileage remains a critical aspect in the realm of electric vehicles (EVs). Among the various factors influencing battery energy consumption during operation, aerodynamic force emerges as a primary contributor. Mitigating this force holds the key to substantial improvements in driving mileage, making it a focal point of exploration in this research. The impact of aerodynamic force extends beyond mere energy consumption; it intricately influences battery size, vehicle mileage, performance, stability, and passenger comfort. Navigating this multifaceted terrain poses a formidable challenge for automotive engineers engaged in the development of electric vehicles. This study undertakes the optimization of rear-end design factors for a hatchback electric vehicle, specifically addressing drag, lift, and aerodynamic noise objectives. Five critical geometric factors of the rear end – Rear Spoiler Length, Rear Spoiler Angle, Rear Diffuser Angle, Boat Tail Angle, and 5th Door Height – are identified as key design parameters. The interplay of these factors and their impact on objectives is systematically investigated through a Design of Experiment (DoE) approach. To enhance the efficiency of the investigation, a fractional factorial design method is utilized, effectively reducing the number of individual case studies. The formulation of regression equations, capturing the essence of significant terms for each objective, lays the groundwork for a subsequent multi-objective optimization process. This optimization, driven by the maximization of a composite desirability function, identifies optimal levels for each design factor. The research culminates in the selection of a model with optimum rear-end factors based on a comprehensive evaluation of drag, lift, and aerodynamic noise objectives. The aerodynamic performance surrounding this optimal model is intricately described, offering valuable insights into the holistic impact of the chosen design parameters on the electric vehicle's aerodynamics.

Publisher

Research Square Platform LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3