Browse Topic: Chassis stiffness
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.
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 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.
Nowadays, customers expect excellent cabin insulation and superior ride comfort in electric vehicles. OEMs focus on fine tuning the suspension system in electric vehicle to isolate the road induced shocks which finally offers superior ride quality. This paper focuses on enhancing the ride comfort by reducing the road excitation which originates mainly due to road inputs. Higher steering wheel vibration is perceived on the test vehicle on rough road surfaces. To determine the predominant force transfer path, Multi reference Transfer Path Analysis (MTPA) is performed on the front and rear suspension. Based on the finding from MTPA, various recommendations are explored and the effect of each modification is discussed. Apart from this, Operational Deflection Shape (ODS) analysis is used to determine the deflection shape on the entire steering system . Based on ODS findings, recommendations like dynamic stiffness improvements on the steering column and steering wheel are explored and the impact on the steering wheel vibration is discussed. With all the counter measures proposed, steering wheel vibration levels are reduced by ~ 7 dB . Component level modal targets are proposed to avoid the vibration concern due to road excitation.
This paper focuses on the cabin sound quality refinement and the tactile vibration reduction during horn application in the electric vehicle. A loud cracking sound inside the cabin and higher accelerator pedal vibration are perceived while operating the horn. Sound diagnosis is carried out to find out the frequencies causing the cracking noise. Transfer path analysis is conducted to identify the nature of noise and the predominant path through which forces transfer. Based on finding from TPA, various recommendations are evaluated which reduced the noise to a certain extent. Operational Deflection Shape (ODS) is conducted on the horn mounting bracket and on the body to identify the component having higher deflection at the identified frequencies. Recommendations like DPDS improvement on the horn bracket and the body is assessed and the effect of each outcome is discussed. With all the recommendations proposed, the cabin noise levels are reduced by ~ 8 dB (A) and the accelerator pedal vibration levels are reduced by ~ 40%. Sound quality parameter which needs to be considered during the horn selection is explained. The modal criteria which must be taken into account during development phase to avoid the horn cracking noise and tactile vibration is also proposed.
Higher road noise is perceived in the cabin when the test vehicle encounters road irregularities like bump or pothole in the public roads. The transfer of transient road inputs inside the body caused objectionable cabin noise. Measurements are conducted at different road surfaces to identify the patch where the objective data well correlated with the noise measured at the public road. Wavelet analysis is carried out to identify the frequency zones since the events are transient in nature. TPA is carried out in time domain to identify the nature of the noise and the dominant path through which the transient road forces are transferring inside the body. Based on the outcome of TPA, various countermeasures like reduction of dynamic stiffness of suspension bushes, TMDs on the path are proposed to reduce the structure borne noise. Criteria which need to be considered for reduction of cabin noise due to transient road inputs is also discussed.
The increasing adoption of electric vehicles (EVs) has intensified the demand for advanced elastomeric materials capable of meeting stringent noise, vibration and harshness (NVH) requirements. Unlike internal combustion engine (ICE) vehicles, EVs lack traditional masking noise generated by the powertrain. In the automotive industry, the dynamic stiffness of elastomers in internal combustion engines has traditionally been determined using hydraulic test rigs, with test frequencies limited to a maximum of 1,000 Hz. Measurements above this frequency range have not been possible and are conducted only through computerized FE or CAE calculation models. Electric drive systems, however, generate distinct tonal noise components in the high-frequency range up to 10,000 Hz, which are clearly perceptible even at low sound pressure levels. Consequently, the dynamic stiffness characteristics of elastomers up to 3,000 Hz are critical for optimizing NVH performance in EVs. This study focuses on high-frequency dynamic stiffness testing of automotive elastomers using a specialized high-frequency test rig. According to ISO 10846-1 [1], there are two methods for determining the dynamic stiffness of elastomers: the direct method (part 2) and the indirect method (part 3). This paper presents measurements carried out using the direct method, employing an electrodynamic shaker and applying static preload conditions. The objective is to accurately determine the frequency-dependent dynamic stiffness and damping properties of elastomeric components, such as engine mounts, bushings, and isolators, which play a crucial role in mitigating structure-borne noise and vibrations.
Rubber components are an important part of the suspension system of high-speed trains, and the complex nonlinear characteristics of rubber parts have a significant impact on the vehicle dynamic performance. This paper establishes a nonlinear dynamics model of the liquid composite swivel arm positioning node, which can reflect the dynamic stiffness and dynamic damping characteristics of the rubber components that change nonlinearly with the frequency and amplitude, and also has a fast calculation speed. The vehicle dynamics simulation model considering the longitudinal stiffness nonlinear characteristics of the arm node is established, and the influence of the stiffness nonlinearity of the liquid composite arm positioning node on the dynamic performance of the vehicle, such as straight-line stability and curve passing ability, is studied in depth through numerical simulation.
The continuous improvement of validation methodologies for mobility industry components is essential to ensure vehicle quality, safety, and performance. In the context of mechanical suspensions, leaf springs play a crucial role in vehicle dynamics, comfort, and durability. Material validation is based on steel production data, complemented by laboratory analyses such as tensile testing, hardness measurements, metallography, and residual stress analysis, ensuring that mechanical properties meet fatigue resistance requirements and expected durability. For performance evaluation, fatigue tests are conducted under vertical loads, with the possibility of including "windup" simulations when necessary. To enhance correlation accuracy, original suspension components are used during testing, allowing for a more precise validation of the entire system. Additionally, dynamic stiffness measurements provide valuable input for vehicle dynamics and suspension geometry analysis software, aiding in predictive modeling and optimization of suspension behavior. The implementation of this structured and comprehensive validation methodology, already adopted by companies in the mobility industry, ensures the development of reliable and high-performance products. This approach is particularly beneficial for commercial suspensions used in road transport of cargo and passengers, addressing industry challenges related to safety, durability, and efficiency. By improving validation techniques, the mobility industry strengthens its ability to innovate, comply with regulatory standards, and meet the growing demand for more robust and efficient vehicle suspension systems.
Electric vehicles (EVs) are particularly susceptible to high-frequency noise, with rubber eigenmodes significantly influencing these noise characteristics. Unlike internal combustion engine (ICE) vehicles, EVs experience pronounced variations in dynamic preload during torque rise, which are substantially higher. This dynamic preload variation can markedly impact the high-frequency behaviour of preloaded rubber bushings in their installed state. This study investigates the effects of preload and amplitude on the high-frequency dynamic performance of rubber bushings specifically designed for EV applications. These bushings are crucial for vibration isolation and noise reduction, with their role in noise, vibration, and harshness (NVH) management being more critical in EVs due to the absence of traditional engine noise. The experimental investigation examines how preload and excitation amplitude variations influence the dynamic stiffness, damping properties, and overall performance of rubber bushings under high-frequency excitation. The research methodology involves subjecting rubber bushings to various preload conditions and excitation amplitudes across relevant high-frequency domains. The findings reveal significant dependencies of dynamic characteristics on preload and amplitude, identifying critical thresholds where performance is notably affected. These insights are pivotal for optimizing rubber bushings in EVs, contributing to improved ride quality, enhanced NVH performance, and increased vehicle durability.
The half vehicle spindle-coupled multi-axial input durability test has been broadly used in the laboratory to evaluate the fatigue performance of the vehicle chassis systems by automotive suppliers and OEMs. In the lab, the front or rear axle assembly is usually held by fixtures at the interfaces where it originally connects to the vehicle body. The fixture stiffness is vital for the laboratory test to best replicate the durability test in the field at a full vehicle level especially when the subframe of the front or rear axle is hard mounted to the vehicle body. In this work, a multi-flexible body dynamics (MFBD) model in Adams/Car was utilized to simulate a full vehicle field test over various road events (rough road, braking, steering). The wheel center loads were then used as inputs for the spindle coupled simulations of the front axle with a non-isolated subframe. Three types of fixtures including trimmed vehicle body, a rigid fixture with softer connections and a rigid fixture with stiffer connections are considered in the front axle simulations. The strain response on the chassis structural components is compared from the full vehicle on-field simulation to the half vehicle on-rig simulations. Results indicate that the trimmed vehicle body test is similar to the full vehicle test in strain response only for some components and events. The impact of the boundary difference (inertia-reacted in full vehicle test verses fixed-reacted half vehicle test) is substantial. It was also found that there were minor differences in strain response among the two types of rigid fixtures for most of the cases. It is concluded that the fixture stiffness impact is relatively minor if they are not able to exactly match the vehicle body stiffness. The study provides valuable insights for engineers to select the appropriate fixtures for the half vehicle spindle coupled multi-axial durability test.
Customers are expecting higher level of refinement in electric vehicle. Since the background noise is less in electric vehicle in comparison with ICE, it is challenging for NVH engineers to address even minor noise concerns without cost and mass addition. Higher boom noise is perceived in the test vehicle when driven on the coarse road at a speed of 50 kmph. The test vehicle is rear wheel driven vehicle powered by electric motor. Multi reference Transfer Path Analysis (TPA) is conducted on the vehicle to identify the path through which maximum forces are entering the body. Based on the findings from TPA, solutions like reduction in the dynamic stiffness of the suspension bushes are optimized which resulted in reduction of noise. To reduce the noise further, Operational Deflection Shape (ODS) analysis is conducted on the entire vehicle to identify the deflection shapes of all the suspension components and all the body panels like floor, roof, tailgate, dash panel, quarter panel and doors at the problematic frequency. Based on ODS, the components having higher deflections at the problematic frequency is identified. Modifications are proposed to improve the dynamic stiffness of the structure at the problematic frequency and the contribution of each modification for cabin noise reduction is discussed. Solutions like tuned mass dampers (TMD) on suspension components are explored and the critical parameter which should be considered to get maximum reduction in noise with TMD is also discussed. With all the modifications, the noise levels are reduced by 5 dB (A) at problematic frequency. NVH criteria which should be considered related to suspension system to avoid boom noise concern in electric vehicle is also discussed.
Due to stringent emission norms, all OEMs are shifting focus from Internal combustion engine (ICE) to Electric vehicle (EV). NVH refinement of EVs is challenging due to less background noise in EVs in comparison with ICE vehicles. Motor whine noise is perceived inside cabin till the speed of 20 kmph. Vehicle is powered by electric powertrain (EPT). Electric powertrain is connected to the subframe with the help of three powertrain mounts. Subframe is connected to the body with the help of four mounts. With the help of Transfer Path Analysis (TPA), it is identified that the noise is structure borne and the dominant path is identified. By optimizing the stiffness of the EPT mounts, the structure borne noise levels are reduced. But reducing the stiffness of EPT mount deteriorated the road noise levels. The reason behind deterioration of road noise is investigated. The performance of double isolation of EPT is compared with single isolation of EPT with respect to both road and motor noise. Modal criteria which needs to be considered for EPT and subframe mount development in design stage with respect to road noise and structure borne motor noise is also discussed.
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