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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.

Powertrain mounts are one of the important design characteristics of a vehicle. Powertrain is mostly mounted to the front subframe and once installed in a vehicle, powertrain mounting has an important role in determining the vehicle vibration characteristics. A good mounting system isolates engine input vibration from the vehicle body and minimizes the effect of road inputs to the customer. This paper discusses results of several dynamic models as they relate to noise, vibration and harshness (NVH) and compares the accuracy of these models. Various powertrain models are studied and their accuracy in comparison with full a vehicle model is discussed.

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 scope of this paper is to describe how system level NVH targets are cascaded down to sub system level targets using the technique of Transfer Path Analysis (TPA). In the early stages of a vehicle design program target vehicles for the new vehicle are selected based on their subjective Noise, Vibration, and Harshness (NVH) performance. A reference vehicle for the new product will be selected which will be used as a baseline vehicle for the whole vehicle program. Noise and vibration measurements will be taken on both the reference and target vehicles under multiple load conditions. The simulation target for the new product will be derived from the measurements of reference vehicle, measurements of target vehicle, and the simulation of reference vehicle model. Reverse Transfer Path Analysis tools will be used to quantify the subsystem targets for the new vehicle based on the simulation targets and design intent simulation models of new product.

Sunroof is placed in certain high-end vehicles to give user a better driving experience. All automakers are searching alternatives to reduce weight and cost in the vehicle, in which sunroofs are also impacted. Some alternatives are already applied, as a honeycomb paper used in some sunshades that presents benefits, as less weight and with a good cost reduction. Although, due the reduced weight for this part produced in this material, it shows more susceptibility to reproduce the vibration that vehicle propagates in movement, especially in bad condition roads. The sunroof assembly is dependent of the roof reinforcement and roof skin, but in this special case, the validation could be done in the components itself because the interaction of the sunshades is directly dependent of the other sunroof parts, as rails and front frame.

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 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.

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.

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.

Noise and vibration signals which are stationary are frequently analyzed for frequency content using Fourier Transform methods. Frequency content can be clearly displayed, but temporal characteristics of signals can easily be obscured in a frequency spectrum. Several commonly available methods of analyzing nonstationary signals are available, such as short-time Fourier Transform and wavelet analysis. Smearing of data in the time and/or frequency domains leads to limited usefulness of these methods in analyzing rapidly varying signals. This also applies to stationary signals with perceivable temporal characteristics. The Wigner Distribution is a time-frequency analysis which can analyze rapidly varying signals and show the effects of rapid changes in signal characteristics. It is appealing because it fully preserves all the information present in the original signal.

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.

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.

Objective of this project was to establish very comprehensive, robust vehicle model of three wheeler electric vehicle. This was achieved by simulating vehicle operation and performance on standard as well as realistic driving cycles. Purpose of building a virtual electric three wheeler vehicle model was to validate the mathematical model with respect to real time road load testing. Electric 3 wheeler vehicle model was built in Ricardo-IGNITE. This model includes advanced vehicle which consists of detailed vehicle parameters like mass & CG locations, suspension, brakes and aerodynamic input parameters. Also other parameter includes road load resistances, rotational, transnational inertial effects and power-train efficiencies and detailed battery and motor model with its thermal & inertial properties respectively. Simulation results of performance and range are validated by actual vehicle testing on dynamo-meter and road for standard and realistic driving-cycles respectively.

This paper presents an integrated design/simulation/test approach for evaluating the sound quality of exhaust noise as early as possible in the exhaust system design and development process. A time domain engine/exhaust simulation program is used to calculate the engine order content of the tailpipe radiated noise from an odd fire V-10 exhaust system. Both steady state and transient conditions are simulated and sound files generated for exhaust sound quality evaluation. To increase the realism of played back sounds, the predicted engine orders are mixed with synthesized or recorded background noise for both steady state and transient conditions. These alternative approaches will be described and evaluated for technical feasibility and sound quality.

The sound field in a vehicle is one of the most complex environments being a mixture of multiple, correlated and uncorrelated sound sources. The simulation of vehicle interior sound has traditionally been produced by combining multiple test results where the influence of one source is enhanced while the other sources are suppressed, such as towing the vehicle on a rough surface for road noise, or measuring noise in a wind tunnel. Such methods are costly and provide inherent inaccuracies due to source contamination and lack of synchronization between sources. In addition they preclude the addition of analytical predictions into the simulation. The authors propose an alternative approach in which the component sounds are decomposed or separated from a single operating measurement and which provide the basis for accurate sound synthesis.

Elastomers are traditionally designed for use in applications that require specific mechanical properties. Unfortunately, these properties change with respect to many different variables including heat, light, fatigue, oxygen, ozone, and the catalytic effects of trace elements. When elastomeric mounts are designed for NVH use in vehicles, they are designed to isolate specific unwanted frequencies. As the elastomers age however, the desired elastomeric properties may have changed with time. This study looks at the variability seen in new vehicle engine mounts and how the dynamic properties change with respect to miles accumulated on fleet and durability test vehicles.

A fuel-cell-powered vehicle requires a plentiful supply of hydrogen to achieve good performance. This can be produced from methanol via an on-board reformer and gas clean-up unit. Since the reformer can take several minutes to reach its operating temperature, it is initially necessary to provide an alternative power source, such as a battery or ultra-capacitor, in order to drive the vehicle. This paper describes the use of a fuel cell vehicle simulation to predict behavior over a drive cycle from a cold start and to evaluate different warm-up control strategies in terms of performance and fuel efficiency.