Browse Topic: Chassis
For mature virtual development, enlarging coverage of performances and driving conditions comparable with physical prototype is important. The subjective evaluation on various driving conditions to find abnormal or nonlinear phenomena as well as objective evaluation becomes indispensable even in virtual development stage. From the previous research, the road noise had been successfully predicted and replayed from the synthesis of system models. In this study, model based NVH simulator dedicated to virtual development have been implemented. At first, in addition to road noise, motor noise was predicted from experimental models such as blocked force and transfer function of motor, mount and body according to various vehicle conditions such as speed and torque. Next, to convert driver’s inputs such as acceleration and brake pedal, mode selection button and steering wheel to vehicle’s driving conditions, 1-D performance model was generated and calibrated. Finally, the audio and visual
A test and signal processing strategy was developed to allow a tire manufacturer to predict vehicle-level interior response based on component-level testing of a single tire. The approach leveraged time-domain Source-Path-Contribution (SPC) techniques to build an experimental model of an existing single tire tested on a dynamometer and substitute into a simulator vehicle to predict vehicle-level performance. The component-level single tire was characterized by its acoustic source strength and structural forces estimated by means of virtual point transformation and a matrix inversion approach. These source strengths and forces were then inserted into a simulator vehicle model to predict the acoustic signature, in time-domain, at the passenger’s ears. This approach was validated by comparing the vehicle-level prediction to vehicle-level measured response. The experimental model building procedure can then be adopted as a standard procedure to aid in vehicle development programs.
Road noise caused by road excitation is a critical factor for vehicle NVH (Noise, Vibration, and Harshness) performance. However, assessing the individual contribution of components, particularly bushings, to NVH performance is generally challenging, as automobiles are composed of numerous interconnected parts. This study describes the application of Component Transfer Path Analysis (CTPA) on a full vehicle to provide insights into improving NVH performance. With the aid of Virtual Point Transformation (VPT), blocked forces are determined at the wheel hubs; afterward, a TPA is carried out. As blocked forces at the wheel hub are independent of the vehicle dynamics, these forces can be used in simulations of modified vehicle components. These results allow for the estimation of vehicle road noise. To simulate changes in vehicle components, including wheel/tire and rubber bushings, Frequency-Based Substructuring (FBS) is used to modify the vehicle setup in a simulation model. In this
The recent addition of fully electric powertrains to propulsion system options has increased the relevance of sound and vibration from electric motors and gearboxes. Electrified beam axles require different metrics from conventional beam axles for noise and vibration because they have multiple sources of vibration energy, including an electric motor and a reduction gearbox. Improved metrics are also driven by the stiff suspension connections and lack of significant isolation compared to electric drive units. Blocked force is a good candidate because it can completely characterize the vibration energy transmitted into a receiver and is especially useful because it is theoretically independent of the vehicle-side structure. While the blocked force methodology is not new, its application to beam axles is relatively unexplored in the literature. This paper demonstrates a case study of blocked force measurement of an electrified beam axle with a leaf spring suspension. The axle was tested
In the highly competitive automotive industry, optimizing vehicle components for superior performance and customer satisfaction is paramount. Hydrobushes play an integral role within vehicle suspension systems by absorbing vibrations and improving ride comfort. However, the traditional methods for tuning these components are time-consuming and heavily reliant on extensive empirical testing. This paper explores the advancing field of artificial intelligence (AI) and machine learning (ML) in the hydrobush tuning process, utilizing algorithms such as random forest, artificial neural networks, and logistic regression to efficiently analyze large datasets, uncover patterns, and predict optimal configurations. The study focuses on comparing these three AI/ML-based approaches to assess their effectiveness in improving the tuning process. A case study is presented, evaluating their performance and validating the most effective method through physical application, highlighting the potential
This document describes an SAE Recommended Practice for Automatic Emergency Braking (AEB) system performance testing which: Establishes uniform vehicle level test procedures Identifies target equipment, test scenarios, and measurement methods Identifies and explains the performance data of interest Does not exclude any particular system or sensor technology Identifies the known limitations of the information contained within (assumptions and “gaps”) Is intended to be a guide toward standard practice and is subject to change on pace with the technology Focuses on “Vehicle Front to Rear, In Lane Scenarios” expanded to include additional offset impacts This document describes the equipment, facilities, methods, and procedures needed to evaluate the ability of Automatic Emergency Braking (AEB) systems to detect and respond to another vehicle, in its forward path, as it is approached from the rear. This document does not specify test conditions (e.g., speeds, decelerations, clearance gaps
This SAE Recommended Practice establishes uniform procedures for evaluating conformity between the actual and target drive speeds for chassis dynamometer and on-road testing utilizing standard fuel economy/energy consumption and emissions drive schedules.
This SAE Recommended Practice covers minimum requirements for air brake hose assemblies made from reinforced elastomeric hose and suitable fittings for use in automotive air brake systems, including flexible connections from frame to axle, tractor to trailer, trailer to trailer, and other unshielded air lines with air pressures up to 1 MPa, that are exposed to potential pull or impact. This hose is not to be used where temperatures, external or internal, fall outside the range of -40 to +100 °C. Provisions for extreme low temperature performance testing to -54 °C are included in the document.
The use of drum brakes in Battery Electric Vehicles (BEVs) offers numerous benefits, including energy efficiency, reduced brake dust emissions, and reliable performance under challenging weather conditions. The capability of regenerative braking reduces the friction brake application frequency in BEVs and therefore the brakes can be prone to corrosion and performance degradation especially considering conventional disc brake systems. The closed design of a drum brake prevents corrosion of the friction-components by sealing out water, dirt or snow. A common sealing concept is performed with a labyrinth between the gap of the rotating drum and the axle mounted backplate. A hermetical isolation of water and snow ingress into the drum cannot be achieved with this concept, so additional aerodynamic measures are necessary to deflect the air/water path and protect the inner brake components. Additionally, interfaces like wheel cylinders, electric park brake parts, brake shoe pins, and axle
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