| dc.contributor.author | Khan, Mumin | |
| dc.contributor.author | Cameron, Bruce | |
| dc.date.accessioned | 2026-02-04T15:59:24Z | |
| dc.date.available | 2026-02-04T15:59:24Z | |
| dc.date.issued | 2025-08-12 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/164725 | |
| dc.description.abstract | The penetration and variety of Battery Electric Vehicles (BEVs) in the automotive sector have been growing rapidly. While there is substantial research on hybrid ICE-battery vs. battery-only choices, little work has examined whether a dominant design for BEVs is emerging, as predicted by the innovation literature. This study provides a comprehensive exploration of BEV architectures, examining the influence of individual architectural decisions on vehicle performance and market prevalence. This study utilizes multivariate linear regression to analyze a curated dataset of global BEV models from 2022 and 2023, focusing on candidate architectural decisions such as battery cathode composition, battery voltage choice, number of motors, and drive layout. Our research aims to identify potential dominant designs by assessing their impact on performance metrics. The analysis then leverages statistical tools to evaluate the correlation between these architectural decisions and vehicle performance, using range as a primary indicator of consumer appeal. Findings from this research indicate significant variance in the adoption of specific BEV architectures, suggesting that the market has not yet consolidated down to a dominant design. We observe, however, that range is most strongly influenced by the architectural decisions for battery capacity, drive type, and motor type. | en_US |
| dc.language.iso | en | |
| dc.publisher | Taylor & Francis | en_US |
| dc.relation.isversionof | https://doi.org/10.1080/09544828.2025.2541150 | en_US |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
| dc.source | Taylor & Francis | en_US |
| dc.title | What determines EV architecture? An analysis of the most influential battery electric vehicle design decisions from market data | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Khan, M., & Cameron, B. (2025). What determines EV architecture? An analysis of the most influential battery electric vehicle design decisions from market data. Journal of Engineering Design, 1–17. | en_US |
| dc.contributor.department | System Design and Management Program. | en_US |
| dc.relation.journal | Journal of Engineering Design | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2026-02-04T15:50:59Z | |
| dspace.orderedauthors | Khan, M; Cameron, B | en_US |
| dspace.date.submission | 2026-02-04T15:51:00Z | |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |