A probabilistic optimization approach to vehicle suspension design under uncertainty

Xiaokai Chen; Qinghai Zhao; Yi Lin; Kang Song

doi:10.1007/978-3-642-33795-6_8

A probabilistic optimization approach to vehicle suspension design under uncertainty

Xiaokai Chen^*
, Qinghai Zhao
, Yi Lin
, Kang Song

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology
BAIC Group

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The problem of vehicle development subject to uncertain parameters is of great significance in realistic engineering applications. A probabilistic optimization approach are proposed to deal with the uncertainty and demonstrated in vehicle suspension design application. The uncertainty propagation is realized by Sparse Grid Techniques. As a hierarchical multilevel Multidisciplinary Design Optimization (MDO) method with uncertainty, Probabilistic Analytical Target Cascading (PATC) is enhanced by considering the first two statistical moments of interrelated responses. The proposed methods were demonstrated by a suspension probabilistic optimization problem, and were solved by the proposed PATC and SGNI. Results show that the enhanced PATC has good effectiveness and efficiency.

Original language	English
Title of host publication	Proceedings of the FISITA 2012 World Automotive Congress
Pages	81-93
Number of pages	13
Edition	VOL. 10
DOIs	https://doi.org/10.1007/978-3-642-33795-6_8
Publication status	Published - 2013
Event	FISITA 2012 World Automotive Congress - Beijing, China Duration: 27 Nov 2012 → 30 Nov 2012

Publication series

Name	Lecture Notes in Electrical Engineering
Number	VOL. 10
Volume	198 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	FISITA 2012 World Automotive Congress
Country/Territory	China
City	Beijing
Period	27/11/12 → 30/11/12

Keywords

Multidisciplinary design optimization (MDO)
Probabilistic optimization
Suspension
Uncertainty

Access to Document

10.1007/978-3-642-33795-6_8

Cite this

@inproceedings{d8e6d389d54c47ad955946cc9f736ed5,

title = "A probabilistic optimization approach to vehicle suspension design under uncertainty",

abstract = "The problem of vehicle development subject to uncertain parameters is of great significance in realistic engineering applications. A probabilistic optimization approach are proposed to deal with the uncertainty and demonstrated in vehicle suspension design application. The uncertainty propagation is realized by Sparse Grid Techniques. As a hierarchical multilevel Multidisciplinary Design Optimization (MDO) method with uncertainty, Probabilistic Analytical Target Cascading (PATC) is enhanced by considering the first two statistical moments of interrelated responses. The proposed methods were demonstrated by a suspension probabilistic optimization problem, and were solved by the proposed PATC and SGNI. Results show that the enhanced PATC has good effectiveness and efficiency.",

keywords = "Multidisciplinary design optimization (MDO), Probabilistic optimization, Suspension, Uncertainty",

author = "Xiaokai Chen and Qinghai Zhao and Yi Lin and Kang Song",

year = "2013",

doi = "10.1007/978-3-642-33795-6\_8",

language = "English",

isbn = "9783642337949",

series = "Lecture Notes in Electrical Engineering",

number = "VOL. 10",

pages = "81--93",

booktitle = "Proceedings of the FISITA 2012 World Automotive Congress",

edition = "VOL. 10",

note = "FISITA 2012 World Automotive Congress ; Conference date: 27-11-2012 Through 30-11-2012",

}

Chen, X, Zhao, Q, Lin, Y & Song, K 2013, A probabilistic optimization approach to vehicle suspension design under uncertainty. in Proceedings of the FISITA 2012 World Automotive Congress. VOL. 10 edn, Lecture Notes in Electrical Engineering, no. VOL. 10, vol. 198 LNEE, pp. 81-93, FISITA 2012 World Automotive Congress, Beijing, China, 27/11/12. https://doi.org/10.1007/978-3-642-33795-6_8

A probabilistic optimization approach to vehicle suspension design under uncertainty. / Chen, Xiaokai; Zhao, Qinghai; Lin, Yi et al.
Proceedings of the FISITA 2012 World Automotive Congress. VOL. 10. ed. 2013. p. 81-93 (Lecture Notes in Electrical Engineering; Vol. 198 LNEE, No. VOL. 10).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A probabilistic optimization approach to vehicle suspension design under uncertainty

AU - Chen, Xiaokai

AU - Zhao, Qinghai

AU - Lin, Yi

AU - Song, Kang

PY - 2013

Y1 - 2013

N2 - The problem of vehicle development subject to uncertain parameters is of great significance in realistic engineering applications. A probabilistic optimization approach are proposed to deal with the uncertainty and demonstrated in vehicle suspension design application. The uncertainty propagation is realized by Sparse Grid Techniques. As a hierarchical multilevel Multidisciplinary Design Optimization (MDO) method with uncertainty, Probabilistic Analytical Target Cascading (PATC) is enhanced by considering the first two statistical moments of interrelated responses. The proposed methods were demonstrated by a suspension probabilistic optimization problem, and were solved by the proposed PATC and SGNI. Results show that the enhanced PATC has good effectiveness and efficiency.

AB - The problem of vehicle development subject to uncertain parameters is of great significance in realistic engineering applications. A probabilistic optimization approach are proposed to deal with the uncertainty and demonstrated in vehicle suspension design application. The uncertainty propagation is realized by Sparse Grid Techniques. As a hierarchical multilevel Multidisciplinary Design Optimization (MDO) method with uncertainty, Probabilistic Analytical Target Cascading (PATC) is enhanced by considering the first two statistical moments of interrelated responses. The proposed methods were demonstrated by a suspension probabilistic optimization problem, and were solved by the proposed PATC and SGNI. Results show that the enhanced PATC has good effectiveness and efficiency.

KW - Multidisciplinary design optimization (MDO)

KW - Probabilistic optimization

KW - Suspension

KW - Uncertainty

UR - https://www.scopus.com/pages/publications/84869066901

U2 - 10.1007/978-3-642-33795-6_8

DO - 10.1007/978-3-642-33795-6_8

M3 - Conference contribution

AN - SCOPUS:84869066901

SN - 9783642337949

T3 - Lecture Notes in Electrical Engineering

SP - 81

EP - 93

BT - Proceedings of the FISITA 2012 World Automotive Congress

T2 - FISITA 2012 World Automotive Congress

Y2 - 27 November 2012 through 30 November 2012

ER -

A probabilistic optimization approach to vehicle suspension design under uncertainty

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Keywords

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