Browse Topic: Chassis
This document specifies a universal method of measuring the thickness change of friction materials to determine the effects of temperature. The test applies to both disc and drum-type linings commonly used in hydraulic and air brake systems for automotive or commercial vehicle applications. This document describes several methods for thermal swell and growth. Method A is where the friction material is in contact with a heated surface to simulate the heat input to the pad that occurs during actual usage. Method B uses an oven to heat the freestanding material and is an approximate procedure requiring less instrumentation. Method A is recommended for disc brake pad assemblies, noise insulators, or flat coupons, while Method B is recommended for curved drum brake linings. This document also describes how to test the warmed-up disc brake pads and noise insulators for hot compressibility using Method A.
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a
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This paper presents an analytical approach for identifying suspension kingpin alignment parameters based on screw axis theorem and differential calculation model. The suspension kingpin caster and inclination alignment parameters can produce additional tire force, which affects vehicle handling dynamics. In wheel steering process, the multi-link suspension control arms lead to movement of the imaginary kingpin, which can cause change in suspension kingpin alignment parameters. According to the structure mechanism of commercial vehicle multi-link independent suspension, the kinematics characteristics of imaginary kingpin were analyzed based on the screw axis theorem. The angular velocity and translation velocity vectors were calculated. In order to avoid the influence of bushing deformation, the unique differential identification model was established to evaluate the suspension kingpin alignment parameters, and the identification results were compared with the ADAMS/Car data. The
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
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.
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 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.
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.
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