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Reliability-based robust multi-obj ective optimization of a 5-DOF vehicle vibration model subjected to random road profiles

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Journal of Central South University 2017年第1期24卷 104-113页

作者： Abolfazl Khalkhali Morteza Sarmadi Sina Yousefi automotive simulation and optimal design research laboratory School of Automotive EngineeringIran University of Science&TechnologyTehran 16846-13114Iran

Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum poi

关键词： probabilistic optimization vehicle suspension deterministic optimization ride handling genetic algorithm

Machine learning driven rational design of dual atom catalysts on graphene for carbon dioxide electroreduction

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Nano research 2025年第1期18卷 186-194页

作者： Dongxu Jiao Xinyi Li Mingzi Sun Lin Liu Jinchang Fan Jingxiang Zhao Bolong Huang Xiaoqiang Cui State Key laboratory of automotive simulation and Control School of Materials Science and Engineeringand Key Laboratory of Automobile Materials of MOEJilin UniversityChangchun 130012China Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina College of Chemistry and Chemical Engineering and Key Laboratory of Photonic and Electronic Bandgap MaterialsMinistry of EducationHarbin Normal UniversityHarbin 150025China

The development of high-performance atomiccatalysts for the carbon dioxide reduction reaction(CO_(2)RR)is atime-consuming process due to the complexity of the reactionmechanism and the uncertainty of the active ***,wehave proposed combining density functional theory(DFT)andmachine learning(ML)to investigate the potential of topologicalgraphene-based dual-atom catalysts(DACs)as CO_(2)*** analyzing the ML results,we identify thenumber of d-orbital electrons in the active site as a key factorinfluencing the CO_(2)RR catalytic ***,wepropose a simple descriptor to measure the CO_(2)RR activity ofthese *** findings provide plausible explanations for thesynergistic interactions between bimetallic atoms in CO_(2)RR andallow us to screen the homogeneous Ni-Ni pair as the mostpromising dual-atom *** work offers a fast MLapproach based on limited DFT calculations to predict the mostelectroactive and stable DACs on carbon support for CO_(2)RR,facilitating rapid screening of high-performance dual-atomcatalysts.

关键词： CO_(2)reaction reduction dual-atom catalysts density functional theory machine learning

optimal design and applicability of electric power steering system for automotive platform

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Journal of Central South University 2019年第4期26卷 839-851页

作者： Abolfazl KHALKHALI Mohammad Hassan SHOJAEEFARD Masoud DAHMARDEH Hadi SOTOUDEH automotive simulation and optimal design research laboratory School of Automotive EngineeringIran University of Science and Technology

The ongoing need for better fuel economy and lower exhaust pollution of vehicles has increased the employment of electric power steering(EPS)in *** design of EPS for a product family reduces the development and fabrication costs *** this paper,the TOPSIS method along with the NSGA-Ⅱis employed to find an optimum family of EPS for an automotive platform.A multi-objective optimization problem is defined considering road feel,steering portability,RMS of Ackerman error,and product family penalty function(PFPF)as the conflicting objective *** results for the single objective optimization problems and the ones for the multi-objective optimization problem,as well as two suggested trade-off design points are presented,compared and *** the two suggested points,performance at one objective function is deteriorated by about 1%,while the commonality is increased by 20%–40%,which shows the effectiveness of the proposed method in first finding the non-dominated design points and then selecting the trade-off among the obtained *** results indicate that the obtained trade-off points have superior performance within the product family with maximum number of common parts.

关键词： product family optimization optimal platform design NSGA-Ⅱ TOPSIS electric power steering

optimal design of automotive body B-pillar using simplified finite element model of body-in-prime combined with an optimization procedure

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Journal of Central South University 2022年第12期29卷 3939-3955页

作者： Mehri IZANLOO Abolfazl KHALKHALI automotive simulation and optimal design research laboratory School of Automotive EngineeringIran University of Science and TechnologyTehran 13114-16846Iran

Optimization of an automotive body structure faces the difficulty of having too many design variables and a too large design search space. A simplified model of body-in-prime(BIP) can solve this difficulty by reducing the number of design variables. In this study, to achieve lighter weight and higher stiffness, the simplified model of BIP was developed and combined with an optimization procedure;consequently, optimal designs of automotive body B-pillar were produced. B-pillar was divided into four quarters and each quarter was modelled by one simplified beam. In the optimization procedure, depth, width, and thickness of the simplified beams were considered as the design ***, bending and torsional stiffness were also considered as objective functions. The optimization procedure is composed of six stages: designing the experiments, calculating grey relational grade, calculating signal-to noise ratio,finding an optimum design using Taguchi grey relational analysis, performing sensitivity analysis using analysis of variance(ANOVA) and performing non-dominated sorting and multi-criteria decision making. The results show that the width of lower B-pillar has the highest effect(about 55%) and the obtained optimum design point could reduce the weight of B-pillar by about 40% without reducing the BIP stiffness by more than 1.47%.

关键词： body-in-prime(BIP)model finite element model bending stiffness torsional stiffness B-pillar Taguchi method multi criterion decision-making(MCDM)method

Establishment and Validation for the Theoretical Model of the Vehicle Airbag

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Chinese Journal of Mechanical Engineering 2015年第3期28卷 487-495页

作者： ZHANG Junyuan JIN Yang XIE Lizhe CHEN Chao State Key laboratory of Automobile simulation and Control Jilin University China automotive Technology & research Center

The current design and optimization of the occupant restraint system（ORS） are based on numerous actual tests and mathematic simulations. These two methods are overly time-consuming and complex for the concept design phase of the ORS, though they＇re quite effective and accurate. Therefore, a fast and directive method of the design and optimization is needed in the concept design phase of the ORS. Since the airbag system is a crucial part of the ORS, in this paper, a theoretical model for the vehicle airbag is established in order to clarify the interaction between occupants and airbags, and further a fast design and optimization method of airbags in the concept design phase is made based on the proposed theoretical model. First, the theoretical expression of the simplified mechanical relationship between the airbag＇s design parameters and the occupant response is developed based on classical mechanics, then the momentum theorem and the ideal gas state equation are adopted to illustrate the relationship between airbag＇s design parameters and occupant response. By using MATLAB software, the iterative algorithm method and discrete variables are applied to the solution of the proposed theoretical model with a random input in a certain scope. And validations by MADYMO software prove the validity and accuracy of this theoretical model in two principal design parameters, the inflated gas mass and vent diameter, within a regular range. This research contributes to a deeper comprehension of the relation between occupants and airbags, further a fast design and optimization method for airbags＇ principal parameters in the concept design phase, and provides the range of the airbag＇s initial design parameters for the subsequent CAE simulations and actual tests.

关键词： occupant restraint systems airbag theoretical model concept design

Identification of Maximum Road Friction Coefficient and optimal Slip Ratio Based on Road Type Recognition

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Chinese Journal of Mechanical Engineering 2014年第5期27卷 1018-1026页

作者： GUAN Hsin WANG Bo LU Pingping XU Liang State Key laboratory of automotive simulation and Control Jilin University

The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and ***,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating *** existing investigations on robust identification of maximum road friction coefficient are *** this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip *** instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time *** current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification *** the road type is recognized,the maximum road friction coefficient and optimal slip ratio are *** numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim *** test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in *** proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.

关键词： maximum road friction coefficient optimal slip ratio road type recognition recursive least square

Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2011年第6期12卷 446-452页

作者： Chang-fu ZONG Pan SONG Dan HU State Key laboratory of automotive simulation and Control Jilin University Changchun 130025 China

A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction *** estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is *** of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.

关键词： Vehicle dynamics State estimation and system identification Active safety and passive safety

Control of water contamination on side window of road vehicles by A-pillar section parameter optimization

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Journal of Hydrodynamics 2020年第6期32卷 1138-1150页

作者： Li Xin Xing-jun Hu Jing-yu Wang State Key laboratory of automotive simulation and Control Jilin UniversityChangchun130025China

The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the *** this paper,an effective optimization method is proposed to reduce the water contamination on the side windows of *** accuracy and the efficiency of the numerical simulation are improved by using the lattice Boltzmann method,and the Lagrangian particle tracking *** parameters are constructed on the basis of the occurrence of the water deposition on a vehicle’s side *** water contamination area of the side window and the aerodynamic drag are considered simultaneously in the design process;these two factors are used to form the multi-objective optimization function in the genetic algorithm(GA)*** approximate model,the boundary-seeded domain method,and the GA method are combined in this study to enhance the optimization *** optimization,the optimal parameters for the A-pillar section are determined by setting the boundary to an area of W=7.77 mm,L=1.27 mm and H=11.22 *** side window’s soiling area in the optimized model is reduced by 66.93%,and the aerodynamic drag is increased by 0.41%only,as compared with the original *** is shown that the optimization method can effectively solve the water contamination problem of side windows.

关键词： Water contamination aerodynamic drag A-pillar section parameters multi-objective optimization approximate model genetic algorithm

Carbody Structural Lightweighting Based on Implicit Parameterized Model

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Chinese Journal of Mechanical Engineering 2014年第3期27卷 483-487页

作者： CHEN Xin MA Fangwu WANG Dengfeng XIE Chen State Key laboratory of automotive simulation and Control Jilin University Zhejiang Geely Automobile research Institute Co. Ltd

Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures. However, new materials and processing techniques inevitably lead to higher costs. Also, material substitution and processing lightweighting have to be realized through body structural profiles and locations. In the huge conventional workload of lightweight optimization, model modifications involve heavy manual work, and it always leads to a large number of iteration calculations. As a new technique in carbody lightweighting, the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper. The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization （MDO） in carbody structure, which is impossible in the traditional structure finite element method （FEM） without parameterization. The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage, and it is validated by some structural performance data. The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop. The lightweighting result of body-in-white （BIW） after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting. This paper proposes the integrated method of implicit parameterized model and MDO, which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.

关键词： carbody structural lightweighting implicit parameterization MDO BIW