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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 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.

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.

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.

For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.