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Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

Beside the automotive industry, where 2-cylinder inline engines are catching attention again, twin-cylinder configurations are quite usual in the small engine world. From stationary engines and range-extender use to small motorcycles up to big cruisers and K-Cars this engine architecture is used in many types of applications. Because of very good overall packaging, performance characteristics and not least the possibility of parts-commonality with 4-cylinder engines nearly every motorcycle manufacturer provides an inline twin in its model range. Especially for motorcycle applications where generally the engine is a rigid member of the frame and vibrations can be transferred directly to the rider an appropriate balancing system is required.

Car manufacturers put large efforts into reducing wind noise to improve the comfort level of their cars. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof designs are tested to meet low-frequency buffeting (also known as boom) targets and broadband noise targets for the fully open sunroof with deflector and for the sunroof in vent position. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process.

The use of state of the art simulation tools for effective front-loading of the calibration process is essential to support the additional efforts required by the new Real Driving Emission (RDE) legislation. The process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight into the bounds of the representation domain of engine mean values. This paper focuses on the methodologies for correlating dynamic simulations with emissions data measured during dynamic vehicle operation (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle, with a particular attention for oxides of nitrogen NOx specie. This correlation is performed using simulated tests run within AVL’s mean value engine and engine aftertreatment (EAS) model MoBEO (Model Based Engine Optimization).

In an era of rapidly increasing vehicle electrification, the gasoline engine remains a vital part of the passenger car powertrain portfolio. Lean-burn combustion is a formidable means for reducing the CO2 emissions of gasoline engines but demands the use of sophisticated emissions control. A 2.0 litre turbocharged direct-injection gasoline engine has been developed with a lean homogeneous combustion system matched to a robust lean and stoichiometric-capable exhaust aftertreatment. The aftertreatment system includes an SCR system and a GPF with filtration down to 10 nm particle size. The engine is equipped with a continuously variable valve-lift system, high-tumble ports and a high-energy ignition system; the boosting system comprises a variable geometry turbocharger and a 48 V electrical supercharger. The work reported formed part of the PaREGEn (Particle Reduced, Efficient Gasoline Engines) project under the Horizon 2020 framework programme.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

The advent of 3D displays offers Human-Machine Interface (HMI) designers and engineers new opportunities to shape the user's experience of information within the vehicle. However, the application of 3D displays to the in-vehicle environment introduces a number of new parameters that must be carefully considered in order to optimise the user experience. In addition, there is potential for 3D displays to increase driver inattention, either through diverting the driver's attention away from the road or by increasing the time taken to assimilate information. Manufacturers must therefore take great care in establishing the ‘do’s and ‘don’t's of 3D interface design for the automotive context, providing a sound basis upon which HMI designers can innovate. This paper describes the approach and findings of a three-part investigation into the use of 3D displays in the instrument cluster of a road car, the overall aim of which was to define the boundaries of the 3D HMI design space.

Many electric (EV) and hybrid-electric (HEV) vehicles are designed to operate using only electric propulsion at low road speeds. This has resulted in significantly reduced vehicle noise levels in urban situations. Although this may be viewed by many as a benefit, a risk to safety exists for those who rely on the engine noise to help detect the presence, location and behaviour of a vehicle in their vicinity. In recognition of this, legislation is being introduced globally which will require automotive manufacturers to implement external warning sound systems. A key challenge for premium vehicle manufacturers is the development of a suitable warning sound signature which also conveys the appropriate brand aspirations for the product. A further major difficulty exists when trying to robustly evaluate potential exterior sounds by running large-scale trials in the real world.

The paper describes a zero-dimensional crank angle resolved combustion model which was developed for the analysis and prediction of combustion in compression ignition (CI) engines. The model relies on the multi zone combustion model (MZCM) approach of Hiroyasu. The main sub-models were taken from literature and extended with additional features described in this paper. A special procedure described in a previous paper is used to identify the mechanisms of the combustion process on the basis of the measured cylinder pressure trace. Based on the identified mechanisms the present work concentrates on the analysis of the causal effects that predominantly control the combustion process and the formation of NOx and Soot. The focus lies on the changes of the thermodynamic states and the composition of the reaction zones caused by different emission control strategies.

The internal combustion engine contains several actuators to control engine performance and emissions. These are controlled within the engine ECU and follow a specific operating strategy to achieve objectives such as NOx reduction and fuel economy. However, these two goals are conflicting and a compromise is required. The operating state depends on system constraints such as engine speed, load, temperature levels, and aftertreatment system efficiency. This results in constantly changing target values to stay within the defined limits, especially the legal emission limits. The conventional approach is to use multiple operating modes. Each mode represents a specific compromise and is activated accordingly. Multiple modes are required to meet emissions regulations under all required conditions, which increases the calibration effort. This new control approach uses an artificial neural network to replace the conventional multiple mode approach.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

Due to increasing complexity of engine electronic systems, there is a demand to handle the often more than 10,000 calibration data automatically. Establishing optimized start of injection and EGR tables of a TC DI Diesel engine by conventional methods takes about two weeks of intensive calibration work. By automatic map calibration, this task can be handled in less than 20 hours automatically, with no staff required during optimization. The benefits of automatic calibration therefore are reduced costs and faster response to any changes in parameters, even with complex multidimensional engine calibration problems. The paper describes the optimization method as well as the experimental work on the test stand that produces the results.

When employing in-car active sound generation (ASG) and active noise cancellation (ANC), the accurate knowledge of the vehicle interior sound pressure distribution in magnitude as well as phase is paramount. Revisiting the ANC concept, relevant boundary conditions in spatial sound fields will be addressed. Moreover, within this study the controllability and observability requirements in case of ASG and ANC were examined in detail. This investigation focuses on sound pressure measurements using a 24 channel microphone array at different heights near the head of the driver. A shaker at the firewall and four loudspeakers of an ordinary in-car sound system have been investigated in order to compare their sound fields. Measurements have been done for different numbers of passengers, with and without a dummy head and real person on the driver seat. Transfer functions have been determined with a log-swept sine technique.

The vehicle dynamics control systems are traditionally based upon utilizing wheel brakes as actuators. However, there has been recently strong interest in the automotive industry for introduction of other vehicle dynamics actuators, in order to improve the overall vehicle stability, responsiveness, and agility features. This paper considers various actuators such as active rear and central differentials and active front and rear steering, and proposes design of related yaw rate control systems. Different control subsystems such as reference model, feedback and feedforward control, allocation algorithm, and time-varying controller limit are discussed. The designed control systems are verified and compared by computer simulation for double lane change and slalom maneuvers.

The continuously decreasing emission limits lead to a growing importance of exhaust aftertreatment in Diesel engines. Hence, methods for achieving a rapid catalyst light-off after engine cold start and for maintaining the catalyst temperature during low load operation will become more and more necessary. The present work evaluates several valve timing strategies concerning their ability for doing so. For this purpose, simulations as well as experimental investigations were conducted. A special focus of simulation was on pointing out the relevance of exhaust temperature, mass flow and enthalpy for these thermomanagement tasks. An increase of exhaust temperature is beneficial for both catalyst heat-up and maintaining catalyst temperature. In case of the exhaust mass flow, high values are advantageous only in case of a catalyst heat-up process, while maintaining catalyst temperature is supported by a low mass flow.

A key element to achieving vehicle emission certification for most light-duty vehicles using spark-ignition engine technology is prompt catalyst warming. Emission mitigation largely does not occur while the catalyst is below its “light-off temperature”, which takes a certain time to achieve when the engine starts from a cold condition. If the catalyst takes too long to light-off, the vehicle could fail its emission certification; it is necessary to minimize the catalyst warm up period to mitigate emissions as quickly as possible. One technique used to minimize catalyst warm up is to calibrate the engine in such a way that it delivers high temperature exhaust. At idle or low speed/low-load conditions, this can be done by retarding spark timing with a corresponding increase in fuel flow rate and / or leaning the mixture. Both approaches, however, encounter limits as combustion stability degrades and / or nitrogen oxide emissions rise excessively.

Full hybrid electric vehicles are usually defined by their capability to drive in a fully electric mode, offering the advantage that they do not produce any emissions at the point of use. This is particularly important in built up areas, where localized emissions in the form of NOx and particulate matter may worsen health issues such as respiratory disease. However, high degrees of electrification also mean that waste heat from the internal combustion engine is often not available for heating the cabin and for maintaining the temperature of the powertrain and emissions control system. If not managed properly, this can result in increased fuel consumption, exhaust emissions, and reduced electric-only range at moderately high or low ambient temperatures negating many of the benefits of the electrification. This paper describes the development of a holistic, modular vehicle model designed for development of an integrated thermal energy management strategy.

The HyPACE (Hybrid Petrol Advanced Combustion Engine) project is a part UK government funded research project established to develop a high thermal efficiency petrol engine that is optimized for hybrid vehicle applications. The project combines the capabilities of a number of partners (Jaguar Land Rover, BorgWarner, MAHLE Powertrain, Johnson Matthey, Cambustion and Oxford University) with the target of achieving a 10% vehicle fuel consumption reduction, whilst still achieving a 90 to 100 kW/liter power rating through the novel application of a combination of new technologies. The baseline engine for the project was Jaguar Land Rover’s new Ingenium 4-cylinder petrol engine which includes an advanced continuously variable intake valve actuation mechanism. A concept study has been undertaken and detailed combustion Computational Fluid Dynamics (CFD) models have been developed to enable the optimization of the combustion system layout of the engine.

The shift toward electrification and limitations in battery electric vehicle technology have led to high demand for hybrid vehicles (HEVs) that utilize a battery and an internal combustion engine (ICE) for propulsion. Although HEVs enable lower fuel consumption and emissions compared to conventional vehicles, they still require combustion of fuels for ICE operation. Thus, emissions from hybrid vehicles are still a major concern. Engine starts are a major source of emissions during any driving event, especially before the three-way catalyst (TWC) reaches its light-off temperature. Since the engine is subjected to multiple starts during most driving events, it is important to mitigate and better understand the impact of these emissions. In this study, experiments were conducted to analyze engine starts in a hybrid powertrain on different experimental setup.

State-of-the-art motorcycle engines consist of numerous variable components and require a powerful motor management to meet the growing customer expectations and the legislative requirements (e.g. exhaust and noise emissions, fuel consumption) at the same time. These demands are often competing and raise the level of complexity in calibration. In the racing domain, the optimization requirements are usually higher and test efficiency is crucial. Whilst the number of variables to control is growing, the time to perform an engine optimization remains the same or is even shortened. Therefore, simulation is becoming an essential part of the engine calibration optimization. Considering the special circumstances in racing, involving valuable hardware, as well as extremely short development and calibration iteration loops, only transient testing is possible.