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Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions. Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

If fuel cell vehicles are to compete in the same marketplace as conventional vehicles, then they must provide the consumer with the same, or improved, levels of convenience, comfort, refinement and performance at the same, or lower, price. In 2003/4, several vehicle manufacturers are planning to launch their first commercial fuel cell vehicles onto the market. In this remarkably short timeframe, many systems must be integrated into a vehicle including the fuel cell system plus thermal and water management, cabin heating, ventilation and air-conditioning, control and on-board diagnostics, power electronics, electric motor and gearbox, suspension, steering, braking, refinement and crash protection. This paper presents a range of modelling techniques which allow the user to design and develop key systems, including the power management system, compressed air supply, thermal management and control algorithms.

The growing competition of the automotive market makes more and more necessary the reduction of development time and consequently, the increase of the capacity to quickly respond to the launching of the competitors. One of the most costly phases on the vehicle development process is the field durability test, both in function of the number of prototypes employed and the time needed to its execution. More and more diffused, the fatigue life prediction methods have played an important part in the durability analysis via CAE. Nevertheless, in order they can be reliable and really being able to reduce the development time and cost, they need to be provided with load cases that can accurately represent the field durability tests. This work presents a CAE approach used for light trucks in order to get a reasonable understanding of component durability behavior due to payload increase. In general, road load data is not available for a new payload condition.

The 45RFE is a new generation electronically controlled rear wheel drive automatic transmission. It employs real-time feedback, closed-loop modulation of shift functions to achieve outstanding shift quality and to meet demanding durability goals. It uses no shift valves; all friction element applications are effected with high-flow electro-hydraulic solenoid valves. A unique gear train arrangement of three planetary carriers allows all sun gears and annulus gears to have the same number of teeth respectively and use a common pinion gear in all carriers, resulting in significant manufacturing simplification. The three-planetary system is designed for four forward ratios of 3.00, 1.67, 1.00 and 0.75 and one reverse gear ratio equal to the low gear ratio. A fifth ratio of 1.50 is used only in certain kick-down shift sequences for highway passing. A sixth forward ratio, an additional overdrive ratio of 0.67, is available in the hardware.

The U.S. Environmental Protection Agency, U.S. Department of Transportation's National Highway and Traffic Safety Administration, and the California Air Resources Board have recently released proposed new regulations for greenhouse gas emissions and fuel economy for light-duty vehicles and trucks in model years 2017-2025. These proposed regulations intend to significantly reduce greenhouse gas emissions and increase fleet fuel economy from current levels. At the fleet level, these rules the proposed regulations represent a 50% reduction in greenhouse gas emissions by new vehicles in 2025 compared to current fleet levels. At the same time, global growth, especially in developing economies, should continue to drive demand for crude oil and may lead to further fuel price increases. Both of these trends will therefore require light duty vehicles (LDV) to significantly improve their greenhouse gas emissions over the next 5-15 years to meet regulatory requirements and customer demand.

This study attempts to develop a correlation between an airflow motion number, combustion burn rates, and initial flame kernel development. To accomplish this task, several motion plates were evaluated on a flowbench in order to calculate a motion number that would represent the dynamic motion in the combustion chamber. Afterwards, the plates were tested on a spark ignited engine at several part throttle conditions while gathering cylinder pressure measurements. These cylinder pressure measurements would then yield the combustion burn rates for each plate. In addition to the combustion measurements, the flame kernel growth, velocity and direction of the flame kernel were measured using an AVL Visio-flame. Finally, the data was evaluated and an attempt to correlate the motion number of the plates to the different measurements for describing combustion was made.

The Ethanol-Boosted Direct Injection (EBDI) demonstrator engine is a collaborative project led by Ricardo targeted at reducing the fuel consumption of a spark-ignited engine. This paper describes the design challenges to upgrade an existing engine architecture and the synergistic use of a combination of technologies that allows a significant reduction in fuel consumption and CO₂ emissions. Features include an extremely reduced displacement for the target vehicle, 180 bar cylinder pressure capability, cooled exhaust gas recirculation, advanced boosting concepts and direct injection. Precise harmonization of these individual technologies and control algorithms provide optimized operation on gasoline of varying octane and ethanol content.

For the purposes of analyzing and understanding the general effects of a set of different vehicle attributes on overall crash outcome a fleet model is used. It represents the impact response, in a one-dimensional sense, of two vehicle frontal crashes, across the frontal crash velocity spectrum. The parameters studied are vehicle mass, stiffness, intrusion, pulse shape and seatbelt usage. The vehicle impact response parameters are obtained from the NCAP tests. The fatality risk characterization, as a function of the seatbelt use and vehicle velocity, is obtained from the NASS database. The fatality risk is further mapped into average acceleration to allow for evaluation of the different vehicle impact response parameters. The results indicate that the effects of all the parameters are interconnected and none of them is independent. For example, the effect of vehicle mass on fatality risk depends on seatbelt use, vehicle stiffness, available crush, intrusion and pulse shape.

This paper describes the application of a systematic methodology to the exploratory investigation of fuel economy for a heavy-duty route vehicle with a hybrid powertrain. The analytical study considered parallel hydraulic hybrid, parallel electric and series electric hybrid architectures in addition to the baseline conventional powertrain. The real world driving mission for the target vehicle, a domestic refuse collection truck, was clarified by making vehicle measurements. System simulation was then used as a key tool to support the fuel economy predictions and trade studies.

It is often necessary to dynamically test a big vehicle part such as a rail tip at component level in high speed. Such a big part can be crush tested dynamically using two sled carriers. The methodology is shown and discussed here, and equations are developed to help determine required parameters such as sled velocity and weights. Test results using a truck rail tip are shown and compared to full vehicle test results for correlation.

Truck frame structural performance of body on frame vehicles is greatly affected by crossmember and joint design. While the structural characteristics of these joints vary widely, there is no known tool currently in use that quickly predicts joint stiffness early in the design cycle. This paper will describe a process used to evaluate the structural stiffness of frame joints based on research of existing procedures and implementation of newly developed methods. Results of five different joint tests selected from current production body-on-frame vehicles will be reported. Correlation between finite element analysis and test results will be shown. Three samples of each joint were tested and the sample variation will be shown. After physical and analytical testing was completed, a Design of Experiments approach was implemented to evaluate the sensitivity of joints with respect to gauge and shape modification.

This paper describes a lightweight magnesium intensive automobile body structure concept developed at DaimlerChrysler to support a high fuel-efficiency vehicle project. This body structure resulted in more than 40% weight reduction over a conventional steel structure while achieving significantly improved structural performance as evaluated through CAE simulations. A business case analysis was conducted and showed promising results. One concept vehicle was built for the purpose of demonstrating concept feasibility. The paper also identifies areas for further development to enable such a vehicle to become a production reality at a later time.

During the design process for a vehicle, the CAD surface geometry becomes available at an early stage so that numerical assessment of aerodynamic performance may accompany the design of the vehicle's shape. Accurate prediction requires open grille models with detailed underhood and underbody geometry with a high level of detail on the upper body surface, such as moldings, trim and parting lines. These details are also needed for aeroacoustics simulations to compute wall-pressure fluctuations, and for thermal management simulations to compute underhood cooling, surface temperatures and heat exchanger effectiveness. This paper presents the results of a significant effort to capitalize on the investment required to build a detailed virtual model of a pickup truck in order to simultaneously assess performance factors for aerodynamics, aeroacoustics and thermal management.

In response to environmental and fossil fuel usage concerns, the automotive industry will gradually move from Hybrid Electric Vehicles (HEV) which includes a shift of internal combustion engines toward Zero Emissions Vehicles (ZEV). Refinement is an important aspect in the successful adoption of any new technology and ZEV brings its own NVH challenges owing to the unique dynamic characteristics of the powertrain and driveline system. This paper presents considerations for addressing dynamic driveline NVH issues that are common to 100% electric vehicles; issues that manifest themselves as groans, rattles and clunks. A dynamic torsional analytical model of the powertrain & driveline will be presented. The analytical model served as the baseline for an extensive parametric study using the Genetic Algorithm (GA) technique, whereby the effectiveness of practical countermeasures was investigated.

In automotive industry, all passenger vehicles are treated with damping materials to reduce structure borne noise. The effectiveness of damping treatments depends upon design parameters such as choice of damping materials, locations and size of the treatment. This paper proposes a CAE (Computer Aided Engineering) methodology based on finite element analysis to optimize damping treatments. The developed method uses modal strain-energy information of bare structural panels to identify flexible regions, which in turn facilitates optimization of damping treatments with respect to location and size. The efficacy of the method is demonstrated by optimizing damping treatment for a full-size pick-up truck. Moreover, simulated road noise performances of the truck with and without damping treatments are compared, which show the benefits of applying damping treatment.

This paper is related to a new concept for the steering linkage of light trucks featuring mono-beam front axles. The current configuration of steering systems for those vehicles comprise a worm and sector steering with a Pitman arm connected to a transverse drag link. This last one connects to the steering link that finally steers the left and right wheels. The problem that has been experienced with this system is that, during a braking event, results in a very unfavorable bump steering condition.

The work carried out with external noise insulation has been demanding high importance in vehicle concept since the second External Noise Regulation in Brazil (Conama 001/ 1993). The engineering effort shall increase significantly for near future developments due to the new Regulation (Conama 272 / 2000) with more stringent limits. The effect over vehicle systems beyond noise requirements is not restricted to the addition of shields and insulators. The airflow restriction created by the noise shields surfaces may become a huge cooling issue. This situation is usually observed on trucks designed for tropical markets and submitted to severe environments. Lessons learned in a current development are the basis of the proposed methodology. Vehicle and lab sound intensity noise source ranking tests are suggested as development tools. The paper also presents a pass-by-noise development strategy that includes CAE airflow and cooling management tools.

In automotive testing, a chassis dynamometer is typically used, during cell testing, to evaluate vehicle performance by simulating actual driving conditions. The use of indoor cell testing has the advantage of running controlled tests where the cell temperature and humidity and solar loads can be well controlled. Driving conditions such as vehicle speed, wind speed and grade can be also controlled. Thus, repeated tests can be conducted with minimum test variations. The tractive effort required at the wheels of a vehicle for a given set of operating parameters is determined by taking into account a set of variables which affect vehicle performance. The forces considered in determination of the tractive effort include the constant friction force, variable friction force due to mechanical and tire friction, forces due to inertia and forces due to aerodynamic and wind effects. In addition, forces due to gravity are considered when road grades are simulated.