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Vehicle Handling Indexes Mining and Consistency of Subjective and Objective Evaluation

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Advances in Dynamics of Vehicles on Roads and Tracks (IAVSD 2019)

Abstract

Automobile becomes more and more personalized and humanized on the promise of safety, comfort, energy saving, environmental protection. This trend requires the OEM grasp the consumers’ functional demands and psychological needs clearly, cascade and predict the developing automobile’s performance accurately during the automobile design and development phase, and realize the top down design with modular, digital and intelligent technology. The handling is one of the key performance for vehicle dynamics, and the establishment of vehicle handling evaluation system is essentially a non-linear classification problem with big data, many-to-many and tight coupling. This paper is intended to explore a simple handling objective-index-mining method and establish a good-consistency of handling subjective and objective evaluation. The 12 core handling objective indexes are extracted and the consistency of the handling subjective and objective evaluation is up to 80% once the sample size is over 120 via the proposed mothed, and the reduced order model are derived with consistency of 70%.

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Acknowledgement

Special thanks are due to the National Natural Science Foundation of China [51675217], the Young Elite Scientists Sponsorship Program by CAST [2016QNRC001], the China Automobile Industry Innovation and Development Joint Fund [U1564213] for supporting authors’ research.

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Correspondence to Xinjie Zhang .

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Zhang, X., Tang, S., Guo, K., Chen, D., Li, W., He, K. (2020). Vehicle Handling Indexes Mining and Consistency of Subjective and Objective Evaluation. In: Klomp, M., Bruzelius, F., Nielsen, J., Hillemyr, A. (eds) Advances in Dynamics of Vehicles on Roads and Tracks. IAVSD 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-38077-9_151

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  • DOI: https://doi.org/10.1007/978-3-030-38077-9_151

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38076-2

  • Online ISBN: 978-3-030-38077-9

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