Browse Topic: Suspension systems
Recent advancements in system-level NVH (Noise, Vibration, and Harshness) development methodologies have improved target cascading and enabled more efficient system-level optimization. Dynamic substructuring facilitates the virtual integration and modification of multiple subsystems and the prediction of changes in overall transfer functions. In practical automotive applications, advanced frequency-based substructuring has been applied to virtually modify system parameters, such as mass and stiffness, at multiple points in a target system, allowing prediction of the resulting effects and optimization of parameter changes without physical intervention. This study extends the methodology by introducing an enhanced substructuring approach capable of addressing not only basic parameter modifications but also large-scale structural changes. The proposed process involves identifying the characteristics of a base system assembly and a target subsystem, decoupling the subsystem from the assembly, incorporating structural modifications, and predicting the resulting transfer function changes. The method was validated through two complementary workflows: a fully experimental test-based workflow and a hybrid workflow. The test-based workflow demonstrated the reliability of substructuring operations, decoupling and coupling, by experimentally evaluating the base assembly, the original subsystem, and the structurally modified subsystem. The hybrid workflow replaced the experimental subsystem models with finite element models, thereby demonstrating the feasibility of substructuring numerical subsystem models with a physical system assembly. Together, these workflows are applied to one of automotive suspension subsystems, cross-member, which can establish the accuracy, flexibility, and practical applicability of the proposed method in supporting system-level NVH development and structural optimization.
The rapid electrification of the automotive industry introduces new challenges in noise, vibration, and harshness (NVH). In particular, in a virtual prototyping phase of the e-vehicles development, the rubber mounts are often one of the key elements to be considered when analysing the structure borne noise contributions. Having an accurate experimental characterization of the mount dynamic stiffness curves is therefore very relevant. However, conventional mount characterization methods are often pushed to their limits, partly due to the use of stiffer bushings, and partly because the frequency range of interest is extended toward higher frequencies. When using inverse substructuring, the dynamic stiffness curves can be obtained from frequency response function measurements. The required test setup consists of excitations and responses, located on each side of the mount via dedicated fixtures. The measured frequency response functions are reduced into 6 degrees of freedom representation at the active and passive side of the mount using the classical virtual point transformation. This classical approach assumes the fixtures to behave rigidly. This assumption holds in the lower frequency range, but not anymore in the higher frequency range. In this paper, novel approaches to identify the dynamic stiffness are presented. Namely, an enhanced virtual point transformation that considers flexible fixtures modes is proposed. Those modes may be obtained via finite element modeling or from an experimental modal analysis. Alternatively, a hybrid framework leveraging high-frequency testing and simulation to develop a parametric finite element mount model is presented. The latter approach eliminates the need for fixtures. These methodologies are compared and validated on an automotive rubber mount.
The TiltRotor Aeroelastic Stability Testbed (TRAST) was developed to experimentally investigate whirl-flutter stability of tiltrotor aircraft. Previous wind-tunnel testing focused on configurations representative of current generation tiltrotors utilizing gimballed rotor hubs. The TRAST platform was also designed to support a hingeless rotor system to investigate whirl-flutter mechanisms representative of stiff proprotor configurations. This paper presents analytical whirl-flutter predictions for a hingeless rotor configuration of the TRAST model. Structural mode shapes derived from a NASTRAN finite-element model are combined with comprehensive aeroelastic analyses in CAMRAD II and RCAS. The results show that the dominant whirl-flutter mechanism differs from the gimballed configuration, with instability occurring through the wing in-plane mode rather than the wing vertical bending mode. Parametric studies examining rotor speed, pitch-spring stiffness, rotor flexibility, and diaphragm spring stiffness are conducted to evaluate the sensitivity of the predicted stability boundary. Results indicate that the hingeless configuration is significantly more stable than the equivalent gimballed configuration and exhibits different trends with rotor speed and structural stiffness. These predictions help identify configurations of interest for future wind-tunnel testing and provide insight into whirl-flutter mechanisms for hingeless tiltrotor systems.
With the rapid development of automated driving and the increasing adoption of “zero-gravity” seats, the crash safety of highly reclined occupants has become a critical issue. The current THOR dummy, designed for frontal impacts in the standard upright posture, exhibits limitations when directly applied to reclined seating configurations, including insufficient spinal flexion capability and excessive posterior pelvic rotation. In this study, the thoracolumbar spine kinematics of the THUMS human body model, reconstructed against post-mortem human subject (PMHS) tests, were analyzed. A two-segment linear fitting was employed to characterize a “dummy-like” spinal flexion response, yielding a virtual rotational hinge located near the thoracolumbar joint of the original THOR model. The characteristic rotation angle obtained from THUMS showed a strong linear correlation with the flexion moment of the T12–L1 vertebrae. Based on this relationship, the rotational joint of the THOR dummy was unlocked during impact and assigned a torsional stiffness of 600 Nm/rad. Additional modifications were implemented in the hip region to enhance model applicability. Comparative simulations demonstrated that the modified THOR model achieved closer agreement with PMHS responses than both the Hybrid III and the baseline open-source THOR models. In particular, the posterior pelvic tilt was reduced from approximately 20° in the baseline THOR to about 10° in the modified version. These results indicate that incorporating PMHS-based thoracolumbar flexion characteristics together with targeted hip modifications significantly improves the biofidelity of the THOR dummy for reclined-occupant crash scenarios, providing a solid foundation for future dummy development and safety assessment.
The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to enhanced energy-efficiency, reduced component and system costs, and a cut-down in development time of EVs, enabled by digital-twin-based design methodologies. Beyond these performance gains, SmartCorners establishes the foundational principles of modularity, adaptability, and software integration that underpin the evolution toward SDVs. The role of thermal and cabin comfort control is completely different for EVs and internal combustion engine vehicles, with the latter using waste heat from the combustion of fossil fuels for cabin heating, ventilation, and cooling (HVAC). In EVs the required energy is directly taken from the traction battery and precise thermal and cabin comfort control affecting essential components of the vehicle but also the user-perceived driving experience. These project achievements highlight a critical bridge between innovation and electrification on component-level, and the holistic software-defined mobility systems of the future.
In recent years, premium vehicles have increasingly incorporated suspension systems capable of adjusting ride height. The primary function of these systems is to enable the vehicle to traverse uneven terrain by elevating the chassis, thereby preventing contact between the underbody and the road surface. Notably, air spring-based mechanisms enhance ride comfort by modulating the wheel rate. The system proposed in this study achieves ride height adjustment through vertical displacement of the spring’s lower seat. By constructing a detailed mechanical topology model using a dynamic simulation tool, this research aims to evaluate the feasibility of improving driving performance not only through height regulation but also by actively controlling the vehicle’s posture during motion.
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their degradation characteristics. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental results show that the maximum absolute percentage errors of the DNN model in predicting suspension stiffness and damping coefficients are less than 0.13% and 0.17%, respectively, with average absolute percentage errors below 0.046% and 0.06%. The coefficients of determination (R2) exceed 0.999. The proposed method accurately predicts the trend of key suspension parameters, providing robust data support for health management and maintenance decision-making. This is expected to reduce safety risks and maintenance costs while enhancing overall vehicle performance and reliability.
Performing transportation and exploration tasks on rugged terrain requires both high load-bearing capacity and large suspension stroke. However, the corner module configurations applied to challenging terrain have rarely been explored. This article proposes an integrated framework that combines bionic principles with topology graph–based type synthesis. This framework leads to the creation of a reconfigurable wheel-legged mechanism capable of switching between wheeled locomotion and legged gait modes, which is then implemented as a corner module system. First, inspired by the skeletal–muscular system of the equine leg, a structure–function mapping relationship between the biological system and the mechanical system is established. Second, a multi-loop closed-chain mechanism with biomimetic morphology is represented in the form of graph theory. A configuration atlas of the wheel-legged hybrid mechanism is generated based on the contracted graph and open-loop kinematic chains, and configuration optimization is carried out. Third, on the basis of the optimized configuration, a biomimetic vibration isolation system is integrated. Finally, a corner module system that integrates the reconfigurable wheel-legged mechanism with steering, hub motor is designed, as well as the mechanical structure of modular transporters based on the aforementioned corner modular architecture. The vibration reduction performance and various locomotion modes of the modular transporter are verified by multibody dynamic simulation.
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