Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (4): 43-51.DOI: 10.12068/j.issn.1005-3026.2025.20230293

• Mechanical Engineering • Previous Articles     Next Articles

Modeling and Optimization Design of Vehicle Powertrain System Considering Effect of Auxiliary Components

Yang-jun WU1, Zhen-ping LI2, Hong-liang YAO1, Sheng-dong HAN1   

  1. 1.School of Mechanical Engineering & Automation,Northeastern University,Shenyang 110819,China
    2.China North Vehicle Research Institute,Beijing 100072,China. Corresponding author: YAO Hong-liang,E-mail: hlyao@mail. neu. edu. cn
  • Received:2023-10-20 Online:2025-04-15 Published:2025-07-01

Abstract:

Based on the neural network parameter indentification method and component mode synthesis (CMS), a modeling approach for the nonlinear powertrain system is proposed to investigate the coupled vibrations of the engine and auxiliary components, and the multi-objective optimization design using genetic algorithms is applied to optimize the connection stiffness and damping. Firstly, a neural network-based model was employed to fit the dynamic model of the powertrain system. According to the experimental modal parameters, the genetic algorithms were applied to identify the connection stiffness and damping of the powertrain system. The results showed that the maximum discrepancies between simulated and experimental modal frequencies and damping ratios were -5.98% and -15.72%, respectively. Subsequently, the CMS is employed to reduce the degrees of freedom of the powertrain system, and the engine-equipment coupling vibration response is evaluated. Finally, a multi-objective optimization design was performed to achieve the optimal vibration performance of the auxiliary components. The maximum peak values displacement of the intercooler and air filter for the optimized model decreased by 34.6% and 4.61%, respectively, compared to the original ones.

Key words: powertrain modeling, auxiliary components, neural network, parameter identification, substructure, multi-objective optimization

CLC Number: