Despite advancements in ergonomic comfort assessments in automotive design, optimizing seat dimensions within the constrained spaces of micro-electric cars presents a substantial challenge. In this study, response surface methodology (RSM) is utilized for the ergonomics design of a micro electric car in the conceptual design phase. Specifically, five critical seat dimensions are analyzed: Seatback Angle, Seat Base Angle, Steering Wheel Height from Car Base, Distance from Seat Base to Pedals, and Distance from Seat Base to Steering Wheel. The analysis took place using a 2-level full factorial design L32 orthogonal array. The optimal dimensions are determined using RSM, such as the mean comfort level and signal-to-noise ratio are maximized, and the standard deviation for multiple drivers is minimized. Three regression models are formulated for the three responses, and their adequacy is checked using different methods. The optimal dimensions are obtained and verified through digital human modeling simulation. This research offers practical guidelines for the ergonomic seating design in micro-electric vehicles, enhancing comfort without compromising space efficiency.

    This study creates better car seats for small electric cars, making rides more comfortable.

    It uses a unique mix of methods to find the best seat design, verified by software.

    Findings help car companies design seats that fit more people’s comfort needs.


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    Title :

    Multi-objective ergonomics design model optimization for micro electric cars via response surface methodology


    Additional title:

    Discov Appl Sci


    Contributors:

    Published in:

    Publication date :

    2024-10-15




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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