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This course will present an introduction to vehicle dynamics from a vehicle system perspective. The theory and applications are associated with the interaction and performance balance between the powertrain, brakes, steering, suspensions and wheel and tire vehicle subsystems.  The role that vehicle dynamics can and should play in effective automotive chassis development and the information and technology flow from vehicle system to subsystem to piece-part is integrated into the presentation. Governing equations of motion are developed and solved for both steady and transient conditions.

Take notes! Take the wheel! There is no better place to gain an appreciation for vehicle dynamics than from the driver’s seat. Spend three, intense days with a world-renowned vehicle dynamics engineer and SAE Master Instructor, his team of experienced industry engineers, and the BMW-trained professional driving instructors. They will guide you as you work your way through 12 classroom modules learning how and why vehicles go, stop and turn. Each classroom module is immediately followed by an engaging driving exercise on BMW’s private test track.

Active safety and (ADAS) are now being introduced to the marketplace as they serve as key enablers for anticipated autonomous driving systems. Automatic emergency braking (AEB) is one ADAS application which is either in the marketplace presently or under development as nearly all automakers have pledged to offer this technology by the year 2022. This one-day course is designed to provide an overview of the typical ADAS AEB system from multiple perspectives.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, air temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin.

As environmental concerns have taken the spotlight, electrified powertrains are rapidly being integrated into vehicles across various brands, boosting their market share. With the increasing adoption of electric vehicles, market demands are growing, and competition is intensifying. This trend has led to stricter standards for noise and vibration as well. To meet these requirements, it is necessary to not only address the inherent noise and vibration sources in electric powertrains, primarily from motors and gearboxes, but also to analyze the impact of the spline power transmission structure on system vibration and noise. Especially crucial is the consideration of manufacturing discrepancies, such as pitch errors in splines, which various studies have highlighted as contributors to noise and vibration in electric powertrains. This paper focuses on comparing and analyzing the influence of spline pitch errors on two layout configurations of motor and gearbox spline coupling structures.

Electric vehicles offer cleaner transportation with lower emissions, thus their increased popularity. Although, electric powertrains contribute to quieter vehicles, the shift from internal combustion engines to electric powertrains presents new Noise, Vibration, and Harshness challenges. Unlike traditional engines, electric powertrains produce distinctive tonal noise, notably from motor whistles and gear whine. These tonal components have frequency content, sometimes above 10 kHz. Furthermore, the housing of the powertrain is the interface between the excitation from the driveline via the bearings and the radiated noise (NVH). Acoustic features of the radiated noise can be predicted by utilising the transmitted forces from the bearings. Due to tonal components at higher frequencies and dense modal content, full flexible multibody dynamics simulations are computationally expensive.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

By analyzing the dynamic distortion in all body closure openings in a complete vehicle, a better understanding of the body characteristics can be achieved compared to traditional static load cases such as static torsional body stiffness. This is particularly relevant for non-traditional vehicle layouts and electric vehicle architectures. The body response is measured with the so-called Multi Stethoscope (MSS) when driving a vehicle on a rough pavé road (cobble stone). The MSS is measuring the distortion in each opening in two diagonals. During the virtual development, the distortion is described by the relative displacement in diagonal direction in time domain using a modal transient analysis. The results are shown as Opening Distortion Fingerprint ODF and used as assessment criteria within Solidity and Perceived Quality. By applying the Principal Component Analysis (PCA) on the time history of the distortion, a Dominant Distortion Pattern (DDP) can be identified.

While conventional methods like classical Transfer Path Analysis (TPA), Multiple Coherence Analysis (MCA), Operational Deflection Shape (ODS), and Modal Analysis have been widely used for road noise reduction, component-TPA from Model Based System Engineering (MBSE) is gaining attention for its ability to efficiently develop complex mobility systems. In this research, we propose a method to achieve road noise targets in the early stage of vehicle development using component-level TPA based on the blocked force method. An important point is to ensure convergence of measured test results (e.g. sound pressure at driver ear) and simulation results from component TPA. To conduct component-TPA, it is essential to have an independent tire model consisting of tire blocked force and tire Frequency Response Function (FRF), as well as full vehicle FRF and vehicle hub FRF.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

This 4-week virtual-only experience is conducted by leading experts in the autonomous vehicle industry and academia. You’ll develop an understanding of the fundamentals of AV architecture, including mechatronics, kinematics, and the sense-think-act framework in autonomous systems. The course builds a connection for how robotics are used in autonomous vehicles and provides you with demonstrations, procedures, and the skills necessary to program a robot with basic commands using the Robot Operating System (ROS).

Launch vehicles are vulnerable to aeroelastic effects due to their lightweight, flexible, and higher aerodynamic loads. Aeroelasticity research has therefore become an inevitable concern in the development of the Reusable Launch Vehicle (RLV). RLV is the space analogy of an aircraft, a unanimous solution to achieve more affordable access to space. The lightweight control surface of the RLV signifies the relevance of the study on control effectiveness. It is the capability of a control surface such as an elevon or rudder to produce aerodynamic forces and moments to change the launch vehicle's orientation and maneuver it along the intended flight path. The static aeroelastic problem determines the efficiency of control, aircraft trim behaviour, static stability, and maneuvering quality in steady flight conditions. In this study, static aeroelastic analysis was performed on a typical RLV using MSC/NASTRAN inbuilt aerodynamics.

The aim of this paper is to present a numerical analysis of high-speed flows over a missile geometry. The N1G missile has been selected for our study, which is subjected to a high-speed flow at Mach 4 over a range of Angle of attack (AoA) from 0° to 6°. The analysis has been conducted for a 3-dimensional missile model using ANSYS environment. The study contemplates to provide new insights into the missile aerodynamic performance which includes the coefficient of lift (CL) , coefficient of drag (CD) and coefficient of moment (CM) using computational fluid dynamics (CFD). As there is a lack of availability of data for missile geometry, such as free stream conditions and/or the experimental data for a given Mach number, this paper intends to provide a detailed analysis at Mach 4. As the technology is advancing, there is a need for high-speed weapons (missiles) with a good aerodynamic performance, which intern will benefit in reduction of fuel consumption.