@article {Paua:2025:0736-2935:37,
title = "Optimizing tire NVH performance in electric vehicles: A comprehensive approach using benchmarking, simulation, and validation",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2025",
volume = "271",
number = "2",
publication date ="2025-07-25T00:00:00",
pages = "37-48",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2025/00000271/00000002/art00005",
doi = "doi:10.3397/NC_2025_0011",
author = "Paua, Ketan and Singh, Ram Krishan and Raghupathi, Sundaram and Sundaramoorthy, Ragasruoban and Vikraman, V and Deivasigamani Purushothaman, Balakrishnan",
abstract = "The electrification of mobility is increasingly critical in addressing zero carbon footprint goals set by nations worldwide, with ambitious targets to achieve by 2030. As part of this transition, the development of Electric SUVs (E-SUVs) capable of handling diverse terrains and road
conditions has become a demanding challenge, often requiring extended vehicle development cycles. To streamline this process, many Original Equipment Manufacturers (OEMs) have adopted a new Born-Electric platform development approach to cater to the specific needs of an electric
vehicle. However, in this approach, several challenges are faced to maintain the customer's perceived Noise, Vibration, and Harshness (NVH) quality in E-SUVs. Therefore, it is crucial to follow a systematic & validated approach to NVH optimization, through the entire vehicle development
process to avoid conflicting design changes. In this study, we define a novel methodology of NVH development focusing on Vehicle In-Cabin Noise (ICN) performance by optimizing the tire-induced vibrations. By utilizing advanced simulation techniques, such as CDtire model integration with vehicle
NVH development, tire noise can be effectively analyzed and mitigated before physical tire preparation. This approach ensures efficient NVH optimization while reducing development time and costs for EV platform.",
}