A method for determining an objective evaluation index of a new energy vehicle passenger's car sickness degree

CN122266764APending Publication Date: 2026-06-23CHINA AUTOMOTIVE ENG RES INST +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA AUTOMOTIVE ENG RES INST
Filing Date
2026-03-20
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

In existing technologies, motion sickness testing methods are limited, leading to inaccurate test results and difficulty in effectively quantifying the degree of motion sickness prevention in vehicles, thus affecting passenger comfort.

Method used

Driving data of the test vehicle and physiological data of the test subjects were collected. The correlation coefficient was calculated using the Spearman method to select target indicators, and the motion sickness prevention index was calculated using the entropy weight method. The results were evaluated based on a combination of multi-dimensional data.

Benefits of technology

By integrating multi-dimensional data, key objective evaluation indicators are accurately selected, the degree of motion sickness prevention is quantified, and a scientific basis is provided for vehicle design optimization to improve ride comfort.

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Abstract

The embodiment of the specification discloses a method for determining an objective evaluation index of a new energy vehicle passenger's car sickness degree. The method comprises collecting driving data of a vehicle with a test subject and physiological data of the test subject, and obtaining a pain degree scale score; determining each candidate index based on the driving data and the physiological data, and calculating the correlation coefficient between each candidate index and the pain degree scale score to screen out target indexes; setting weights for the pain degree scale score and each target index based on an entropy weight method, and calculating a car sickness prevention degree index based on the weighted calculation of the pain degree scale score and the value of each target index. In this embodiment, data in multiple dimensions can be integrated to reduce the bias of single dimension test, and key objective evaluation indexes that can represent subjective feelings can be more accurately and rigorously screened out to provide support basis for vehicle research and development optimization and performance test links of enterprises, and help to improve ride comfort and user experience.
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