Multidisciplinary hybrid hierarchical collaborative optimization of electric wheel vehicle chassis integrated system based on driver’s feel

Zhao, Wanzhong; Yang, Zunsi; Wang, Chunyan

doi:10.1007/s00158-017-1801-6

Cited by 15 publications

(4 citation statements)

References 20 publications

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“…Among them, the CO algorithm proposed by Kroo et al [24,25] is considered classic. The CO algorithm is very adaptable and deformable, which can be rapidly deployed and applied to engineering fields, such as aerospace [24,26], vehicle [27,28], engine [29], satellite [30], etc., and many new algorithms have been expanded based on the CO method, such as the introduction of uncertainty and robust into the CO method [31,32], and the combination of multi-objective optimization methods with CO [33][34][35]. However, the original CO model, setting the consistency constraint to zero at the system level, leads to convergence issues in some cases.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

Xie,

Yang,

Wang

et al. 2024

CMES

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To enhance the comprehensive performance of artillery internal ballistics-encompassing power, accuracy, and service life-this study proposed a multi-stage multidisciplinary design optimization (MS-MDO) method. First, the comprehensive artillery internal ballistic dynamics (AIBD) model, based on propellant combustion, rotation band engraving, projectile axial motion, and rifling wear models, was established and validated. This model was systematically decomposed into subsystems from a system engineering perspective. The study then detailed the MS-MDO methodology, which included Stage I (MDO stage) employing an improved collaborative optimization method for consistent design variables, and Stage II (Performance Optimization) focusing on the independent optimization of local design variables and performance metrics. The methodology was applied to the AIBD problem. Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts, thereby accelerating system-level convergence. Meanwhile, Stage II optimization markedly enhanced overall performance. These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.

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Section: Introductionmentioning

confidence: 99%

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

Xie,

Yang,

Wang

et al. 2024

CMES

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“…These can be subsequently solved by adopting dedicated algorithms and numerical tools. Recent examples can be found in literature that apply the MDO approach in the field of electric vehicles: Zhao et al (2018) developed an MDO architecture for the chassis integrated system of electric wheel vehicles. A variablefidelity MDO architecture was considered by Wang et al (2017) to interface the powertrain efficiency with the battery life-time maximization for an electric vehicle.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

Anselma

Niutta

Mainini

et al. 2020

Struct Multidisc Optim

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“…Hierarchical modeling has been widely adopted in many fields, especially in the field involving the integrated design of material-structure systems [3][4][5]. With the advancement of hierarchical modeling, the basic theories and application studies of multilevel design in a hierarchical framework are also developed [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Mapping-Based Hierarchical Sensitivity Analysis for Multilevel Systems With Multidimensional Correlations

Zhu

Liu

et al. 2020

Journal of Mechanical Design

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Abstract Hierarchical sensitivity analysis of multilevel systems is to assess the effect of system's input uncertainties on the variations of system's performance through integrating the sensitivity indices of subsystems. However, it is difficult to deal with the engineering systems with complicated correlations among various variables across levels by using the existing hierarchical sensitivity analysis method based on variance decomposition. To overcome this limitation, a mapping-based hierarchical sensitivity analysis method is proposed to obtain sensitivity indices of multilevel systems with multidimensional correlation. For subsystems with dependent variables, a mapping-based sensitivity analysis, consisting of vine copula theory, Rosenblatt transformation and polynomial chaos expansion technique, is provided for obtaining the marginal sensitivity indices. The marginal sensitivity indices can allow us to distinguish between the mutual depend contribution and the independent contribution of an input to the response variance. Then extended aggregation formulations for local variables and shared variables are developed to integrate the sensitivity indices of subsystems at each level so as to estimate the global effect of inputs on the response. Finally, this paper presents a computational framework that combines related techniques step by step. The effectiveness of the proposed mapping-based hierarchical sensitivity analysis method is verified by a mathematical example and a multiscale composite material.

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Multidisciplinary hybrid hierarchical collaborative optimization of electric wheel vehicle chassis integrated system based on driver’s feel

Cited by 15 publications

References 20 publications

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

Mapping-Based Hierarchical Sensitivity Analysis for Multilevel Systems With Multidimensional Correlations

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