Method for analyzing multi-working condition load and stiffness robustness of suspension system and related equipment

By generating probability distribution maps and virtual samples, combined with Monte Carlo analysis, the load and stiffness response of the suspension system can be accurately predicted, solving the problem of assembly error influence in the design of the suspension system and realizing the robustness optimization of the suspension system under multiple working conditions.

CN122241857APending Publication Date: 2026-06-19FAW CAR CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
FAW CAR CO LTD
Filing Date
2026-02-04
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing suspension system design methods struggle to accurately capture the impact of manufacturing and assembly errors on system robustness, leading to load distribution deviating from design expectations and dispersed stiffness characteristics. Furthermore, existing evaluation methods are time-lagging and costly, failing to provide effective support in the early stages of design.

Method used

By generating probability distribution maps of key assembly locations, virtual assembly samples are created. Monte Carlo analysis is used to simulate the suspension load and stiffness response in a multibody dynamics model. The performance fluctuation range is statistically analyzed, and sensitive parameters are identified and adjusted.

Benefits of technology

This enables the prediction and optimization of multi-condition load distribution and stiffness robustness of the suspension system in advance during the design phase, avoiding physical iteration and reducing design change costs and time.

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Abstract

This invention is primarily applicable to the field of automotive engineering technology. It discloses a method and related equipment for robust analysis of multi-condition loads and stiffness in a suspension system. The method includes the following steps: establishing a probability distribution of errors at key assembly locations based on manufacturing tolerances, generating virtual samples; performing batch simulations of multi-condition loads and stiffness in a suspension dynamics model, statistically obtaining performance fluctuation ranges and outputting a robustness report; and identifying sensitive parameters and adjusting the design accordingly. This application enables early prediction and optimization of the multi-condition load distribution and stiffness robustness of the suspension system under the influence of manufacturing assembly errors during the design phase.
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