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Identification of Automotive Cabin Design Parameters to Increase Electric Vehicles Range, Coupling CFD-Thermal Analyses with Design for Six Sigma Approach
ISSN: 2641-9645, e-ISSN: 2641-9645
Published June 30, 2020 by SAE International in United States
Citation: Piovano, A., Scantamburlo, G., Quaglino, M., and Gautero, M., "Identification of Automotive Cabin Design Parameters to Increase Electric Vehicles Range, Coupling CFD-Thermal Analyses with Design for Six Sigma Approach," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(1):744-751, 2021, https://doi.org/10.4271/2020-37-0032.
The ongoing global demand for greater energy efficiency plays an essential role in vehicle development, especially in the case of electric vehicles (EVs). The thermal management of the full vehicle is becoming increasingly important, since the Heating, Ventilation, and Air Conditioning (HVAC) system has a significant impact on the EV range. Therefore the EV design requires new guidelines for thermal management optimization.
In this paper, an advanced method is proposed to identify the most influential cabin design factors which affect the cabin thermal behavior during a cool down drive cycle in hot environmental conditions. These parameters could be optimized to reduce the energy consumption and to increase the robustness of the vehicle thermal response.
The structured Taguchi’s Design for Six Sigma (DFSS) approach was coupled with CFD-Thermal FE simulations, thanks to increased availability of HPC. The first control factors selected were related to the thermal capacity of the panel duct, dashboard, interior door panels and seats. Surface IR emissivity and solar radiation absorptivity of these components were then added to the study. Car glass with absorptive and reflective glazing were finally included in the study. The design space of 18 vehicle configurations was simulated in spring and hot summer conditions, with steady state thermal simulations. A 2-step optimization was then conducted, trying first to increase the robustness of the cabin response and, secondly, to reduce the equivalent temperature actually felt by passengers. The Verify phase was then conducted on the Best Engineering design emerged by the 2-step optimization, through quasi-transient CFD-Thermal FE analyses. The thermal results were then sent to a CFD 1D energy prediction model, confirming the HV battery energy saving and the extended range reached during the cool down drive cycle.