Baja SAE is an intercollegiate competition where teams design and build a single-seat off-road vehicle that is powered by a small 10 HP Briggs & Stratton engine. Due to this power constraint, it is crucial to optimize the vehicle's weight and performance. The purpose of this paper is to demonstrate the process of simulating, designing, manufacturing, and testing the gearbox of the vehicle. The design process began by creating a vehicle dynamics simulation, which included engine performance, CVT Shifting, tire slipping, vehicle mass, rotational inertia, air drag, rolling resistance, weight shift, and drivetrain efficiency. These calculations predicted acceleration times, top speed, and optimal gear ratio. An often-neglected parameter that was analyzed was the rotational inertia in the drivetrain system. The results showed the effective mass of the vehicle increased 12% above the weight of the vehicle, primarily due to the weight and size of the CVT primary pulley.
Due to the increasing computational power, significant progress has been made over the past decades when it comes to CAD, multibody and simulation software. The application of this software allows to develop products from scratch, or to investigate the static and dynamic behavior of multibody models with remarkable precision. In order to keep the development costs low for highly sophisticated products, more precisely motorcycle rider assistance systems, it is necessary to focus extensively on the virtual prototyping using different software tools. In general, the interconnection of different tools is rather difficult, especially when considering the coupling of a detailed multibody model with a simulation software like MATLAB Simulink. The aim of this paper is to demonstrate the performance of a motorcycle rider assistance algorithm using a cosimulation approach between the free multibody software called FreeDyn and Simulink based on a sophisticated multibody motorcycle model.
The aim of the presented research is to propose and benchmark two brake models, namely the novel dynamic ILVO model and a neural network based regression. These can estimate the evolution of the brake friction between pad and disc under different load conditions, which are typically experienced in vehicle applications. The research also aims improving the knowledge of the underlying mechanism related to the evolution of the BLFC (boundary layer friction coefficient), the reliability of virtual environment simulations to speed up the product development time and reducing the amount of vehicle test in later phases and finally improving brake control functions. With the support of extensive brake dynamometer testing, the proposed models are benchmarked against State-of-the-Art. Both approaches are parametrised to render the friction coefficient dynamics with respect to the same input parameters.
Brakes are the most important safety device in a vehicle, however there are few barriers to manufacture, import, or sell friction materials in most of the countries, including USA. European countries, with the ECE R90 program, are a big exception. International Transport Forum published in 2016 the “Benchmarking of road safety in Latin America” report, it mentions that worldwide 17.5 people in every 100,000 die in road accidents, however Andean countries mortality rate is 23.4 and South American 21.0, considerably higher than the worldwide average.
The particulate emissions of two brake systems where characterized in a dilution tunnel optimized for PM10 measurements. The larger of them employed a fixed caliper (FXC) and the smaller one a floating caliper (FLC). Both used ECE brake pads of the same lining formulation. Measured properties included gravimetric PM2.5 and PM10, Particle Number (PN) concentrations of both untreated and thermally treated (according to exhaust number regulation) particles using Condensation Particle Counters (CPCs) having 23 and 10 nm cut-off sizes, and an Optical Particle Sizer (OPS). The brakes were tested over a novel test cycle developed from the database of the Worldwide harmonized Light-Duty vehicles Test Procedure (WLTP). A series of WLTP tests were performed starting from unconditioned pads, to characterize the evolution of emissions until their stabilization. Selected tests were also performed over a short version of the Los Angeles City Cycle.
Throughout the automotive industry, the application of an integrated electronic booster (IEB) system has been actively applied following with diversify powertrain types and expand autonomous vehicles. Compared to the existing vacuum boosters, the performance advantages of IEB are 1) robustness against environmental changes, 2) rapid hydraulic reactivity, etc., and the advantages of cost / university are 1) flexibility for powertrain changes 2) weight saving 3) package simplification. Although IEB has a great advantage in performance and cost, it still needs a lot of research in various fields to realize the braking feeling, which is the performance of the emotional aspect, similar to the existing system. The braking feeling of the existing system was determined by the mechanical action of the hardware connected from the input device, the brake pedal to the hydraulic line. However, IEB system has a completely different structure from the existing system.
The absence of combustion engine noise pushes increasingly attention to the sound generation from other, even much weaker, sources in the acoustic design of electric vehicles. The present work focusses on the numerical computation of flow induced noise, typically emerging in components of flow guiding devices in electro-mobile applications. The method of Large-Eddy Simulation (LES) represents a powerful technique for capturing most part of the turbulent fluctuating motion, which qualifies this approach as a highly reliable candidate for providing a sufficiently accurate level of description of the flow induced generation of sound.
Assessment of squeak and rattle noise of a car seat using 3D sound intensity measurements Squeak and Rattle (S&R) noises are transient sound events occurring when adjacent parts come into contact, either impacting or sliding. All components and sub-systems integrated in a vehicle may produce noise when excited with certain vibro-acoustic load. S&R noise can be linked to the perceived build quality, durability and even discomfort or annoyance. As a result, car manufacturers have strict regulations to prevent noise issues. Current vibro-acoustic validation tests can vary in complexity from full vehicle simulation to component level tests. Additionally, subjective assessments are often required to locate problematic areas and quantify their relevance. In this paper, S&R noise of a car seat is investigated using 3D sound intensity measurements. A multi-axial shaker is used to drive the seat with a short time-stationary excitation extracted from a road profile.
In general, when a problem occurs in a component, various phenomena appear, and abnormal noise is one of them. The service technicians diagnose the noise through the analysis using hearing and equipment. Depending on their experiences, the analysis time and diagnosis accuracy vary widely. The newly developed AI-based diagnostic technology diagnoses parts that cause abnormal noises within seconds when a noise is input to the equipment. To create a learning model for diagnosis, we collected as many abnormal noises as possible from various parts, and selected good and bad data. This process is very important in the development of diagnostic techniques. Artificial intelligence was learned by deep learning with selected good data. This paper is about the technology that can diagnose the abnormal noises generated from the engine, transmission, drivetrain and PE (Power Electric) parts of the eco-friendly vehicle through the diagnosis model composed of various methods of deep learning.
Over the past decade, there have been many efforts to generate engine sound inside the cabin either in reducing way or in enhancing way. To reduce the engine noise, the passive way, such as sound absorption or sound insulation, was widely used but it has a limitation on its reduction performance. In recent days, with the development of signal processing technology, ANC (Active Noise Control) is been used to reduce the engine noise inside the cabin. On the other hand, technologies such as ASD (Active Sound Design) and ESG (Engine Sound Generator) have been used to generate the engine sound inside the vehicle. In the last ISNVH, Hyundai Motor Company newly introduced ESEV (Engine Sound by Engine Vibration) technology. This paper describes the ESEV Plus Minus that uses engine vibration to not only enhance the certain engine order components but reduce the other components at the same time. Consequently, this technology would produce a much more diverse engine sound.
Nowadays a large proportion of the overall acoustic vehicle development takes place within virtual phases. Increasingly, projects require the auralization of virtual developed acoustics measures, e.g. from the disciplines of electro-acoustic, ride comfort, rolling noise or passive acoustic on dynamic or static driving simulators. In practice it turns out that in addition to engine noise also a realistic reproduction of rolling and wind noise is important. In this article, approaches to synthetic rolling and wind noise generators are discussed. We developed such real-time capable sound generators that are parametrizable according to arbitrary driving conditions. Furthermore, spacial reproduction of the driving sounds is achieved for binaural headphone, as well as for other arbitrary loudspeaker setups, like often found in driving simulators. Derived models and parametrization are based on measurements and recordings from several real vehicles.
The acoustic trim components play an essential role in Noise, Vibration and Harshness (NVH) behavior by reducing both the structure borne and airborne noise transmission while participating to the absorption inside the car and the damping of the structure. Over the past years, the interest for numerical solutions to predict the noise including trim effects in mid frequency range has grown, leading to the development of dedicated CAE tools. Finite Element (FE) models are an established method to analyze NVH problems. FE analysis is a robust and versatile approach that can be used for a large number of applications, like noise prediction inside and outside the vehicle due to different sources or pass-by noise simulation. Typically, results feature high quality correlations. However, future challenges, such as electric motorized vehicles, with changes of the motor noise spectrum, will require an extension of the existing approaches.
Audio CAE is an emerging area of interest for a vehicle OEM, despite the fact that the development of the audio system is often left to a specialized supplier. Especially the questions regarding early stages of the vehicle design, like choosing the possible positions for speakers, deciding the installation details that can influence the visual design, and integration of the low frequency speakers with the body & closures structure, are of interest. Therefore, at VCC, the development of the CAE methodology for audio applications has been undertaken. The long term goal is to enable performing subjective evaluation of sound in a virtual car, and integrating audio evaluation in the NVH simulator. The key to all CAE applications is the loudspeaker model made available in the vibro-acoustic software used within the company. Such a model has been developed, implemented and verified in different frequency ranges and different applications.
Autonomous vehicles must guarantee safety in all road conditions, including driving on wet roads. Aquaplaning (or hydroplaning) is a phenomenon known since the beginning of automotive history, never solved by an active safety system. Currently, no countermeasure system on the market is able to effectively counteract aquaplaning: ABS, ESP or TCS are still inefficient in overcoming this situation. Latest statistical data confirm that the higher percentage of accidents, injuries and deaths are caused by wet road conditions. The aquaplaning happens when the water on the road is too much and the tires start to float causing the instantaneous loss of control. Such phenomenon occurs in human-driven vehicles, with the responsibility of the driver, but in autonomous vehicles (e.g. Level 5), the responsibility for the safety depends on the car and the reduction of the speed is not a solution.
Sharing mobility has led to a reduction of car ownership with consequent decrease in impacts from a multiple economic, social and environmental perspective. One way of reducing emissions in traffic is to establish the use of electric vehicles (EVs). Insufficient knowledge and high uncertainty towards EV technology can represent a barrier to the acceptance of these new forms of mobility. University students are recognized as a prospective customer group for car sharing services, very receptive to technological innovation. This study proposed a methodology to investigate student user profile defining the heterogeneous preferences regarding a mix of attributes of the service design and to assess the impact of car-sharing experience on acceptance of EVs.