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Fuel efficiency and torque performance are two major challenges for highly downsized turbocharged engines. However, the inherent characteristics of the turbocharged SI engine such as negative PMEP, knock sensitivity and poor transient performance significantly limit its maximum potential. Conventional ways of improving the problems above normally concentrate solely on the engine side or turbocharger side leaving the exhaust manifold in between ignored. This paper investigates this neglected area by highlighting a novel means of gas exchange process. Divided Exhaust Period (DEP) is an alternative way of accomplishing the gas exchange process in turbocharged engines. The DEP concept engine features two exhaust valves but with separated function. The blow-down valve acts like a traditional turbocharged exhaust valve to evacuate the first portion of the exhaust gas to the turbine.

Driver behaviour can strongly affect fuel consumption, and driver training in eco-driving techniques has been shown to reduce fuel consumption by 10% on average. However the effects of this training can be short-lived, so there is an apparent need for continuous monitoring of driver behaviour. This study presents a driver advisory tool which encourages eco-driving, and its evaluation in the field. The system, developed by Ashwoods Automotive Ltd (UK) and the University of Bath (UK), is aimed at fleet operators of light commercial vehicles, where the driver is typically a company employee. A significant strength of the system is that it has been designed for easy integration with the vehicle CAN-bus, reducing complexity and cost. By considering the Inertial Power Surrogate (speed times acceleration) the core algorithm is able to identify behaviour which is likely to increase fuel consumption.

Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

In both the marine and power industries there are now a choice of off-the-shelf condition monitoring systems available that utilise artificial intelligence techniques to analyse engine performance data. These systems are proving to be a valuable aid in optimising performance and reducing down-time by assisting with maintenance planning. These systems rely on careful monitoring of an engine's performance, for instance engine speed, fuelling, boost pressure, turbine inlet pressure, turbocharger speed, and exhaust temperature. With this data, they utilise a variety of interpolation and pattern recognition algorithms to compare it with previously recorded data stored in lookup tables. This paper describes how a neural network approach can be used as a cheap alternative for the analysis of this data, greatly reducing the need for such large lookup tables and complex pattern recognition programs.

Vehicle thermal management (VTM) simulations constitute an important step in the early development phase of a vehicle. They help in predicting the temperature profiles of critical components over a drive cycle and identify components which are exceeding temperature design limits. Parts with the highest temperatures in a vehicle with an internal combustion engine are concentrated in the engine bay area. As packaging constraints grow tighter, the components in the engine bay are packed closer together. This makes the thermal protection in the engine bay even more crucial. The fan influences the airflow into the engine bay and plays an important role in deciding flow distribution in this region. This makes modelling of the fan an important aspect of VTM simulations. The challenge associated with modelling the fan is the accurate simulation of the rotation imparted by the fan to the incoming flow. Currently, two modelling approaches are prevalent in the industry.

Engines equipped with pressure charging systems are more prone to knock partly due the increased intake temperature. Meanwhile, turbocharged engines when operating at high engine speeds and loads cannot fully utilize the exhaust energy as the wastegate is opened to prevent overboost. The turboexpansion concept thus is conceived to reduce the intake temperature by utilizing some otherwise unexploited exhaust energy. This concept can be applied to any turbocharged engines equipped with both a compressor and a turbine-like expander on the intake loop. The turbocharging system is designed to achieve maximum utilization of the exhaust energy, from which the intake charge is over-boosted. After the intercooler, the turbine-like expander expands the over-compressed intake charge to the required plenum pressure and reduces its temperature whilst recovering some energy through the connection to the crankshaft.

External Exhaust Gas Recirculation (EGR) provides an opportunity to increase the efficiency of turbocharged spark-ignition engines. Of the competing technologies and configurations, Low-Pressure EGR (LP-EGR) is the most challenging in terms of its dynamic behavior. Only some of the stationary feasible potential can be used during dynamic engine operation. To guarantee fuel consumption-optimized engine operation with no instabilities, a load point-dependent limitation of the EGR rate or alternatively an adaptation of the operating point to the actual EGR rate is crucial. For this purpose, a precise knowledge of efficiency and combustion variance is necessary. Since the operating state includes the actual EGR rate, it has an additional dimension, which usually results in an immense measuring effort.

Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

Increasing demand for dynamically controlled safety features, passenger comfort, and operational convenience in upper class automobiles requires an intensive use of electronic control units including software portions. Modeling, simulation, rapid prototyping, and verification of the software need new technologies to guarantee passenger security and to accelerate the time-to-market of new products. This paper presents the state-of-the-art of the design methods for the development of electronic control unit software at BMW. These design methods cover both discrete and continuous system parts, smoothly integrating the respective methods not merely on the code level, but on the documentation, simulation, and design level. In addition, we demonstrate two modeling and prototyping tools for discrete and continuous systems, namely Statemate and MatrixX, and discuss their advantages and drawbacks with respect to necessary prototyping demands.

The Advanced Lighting Simulation (ALS) is a development tool for systematically investigating and optimizing the Adaptive Light Control (ALC) system to provide the driver with improved headlamps and light distributions. ALS is based on advanced CA-techniques and modern validation facilities. To improve night time traffic safety the BMW lighting system ALC has been developed and optimized with the help of ALS. ALC improves the headlamp illumination by means of continuous adaptation of the headlamps according to the current driving situation and current environment. BMW has already implemented ALC prototypes in real vehicles to demonstrate the advantages on the real road.

Precise, repeatable and representative testing is a key tool for developing and demonstrating automotive fuel and lubricant products. This paper reports on the first findings of a project that aims to determine the requirements for highly repeatable test methods to measure very small differences in fuel economy and powertrain performance. This will be underpinned by identifying and quantifying the variations inherent to this specific test vehicle, both on-road and on Chassis Dynamometer (CD), that create a barrier to improved testing methods. In this initial work, a comparison was made between on-road driving, the New European Drive Cycle (NEDC) and World harmonized Light-duty Test Cycle (WLTC) cycles to understand the behavior of various vehicle systems along with the discrepancies that can arise owing to the particular conditions of the standard test cycles.

The paper presents results from a study into the potential of a complex cycle gas turbine engine, originally investigated by the Ford Motor Company for truck applications in the 1960s, and updated to gauge the possible improvements by raising the efficiencies of its constituent components from the values used in period to more modern levels. To perform this investigation, firstly a spreadsheet model was constructed and the data that Ford made available in the open literature were used to validate it. The methodology used in the model was to balance the power consumed by the compressors (and the auxiliaries where applicable) with that produced by their driving turbines, and to match the thermal power in the heat exchangers with the data provided. Using the quoted lower heating value of the diesel fuel originally used, this approach led to an accuracy in the match of brake specific fuel consumption (in terms of g/kWh) to three places of decimals.

Synthetic fuels for internal combustion engines offer CO2-neutral mobility if produced in a closed carbon cycle using renewable energies. C1-based synthetic fuels can offer high knock resistance as well as soot free combustion due to their molecular structure containing oxygen and no direct C-C bonds. Such fuels as, for example, dimethyl carbonate (DMC) and methyl formate (MeFo) have great potential to replace gasoline in spark-ignition (SI) engines. In this study, a mixture of 65% DMC and 35% MeFo (C65F35) was used in a single-cylinder research engine to determine friction losses in the piston group using the floating-liner method. The results were benchmarked against gasoline (G100). Compared to gasoline, the density of C65F35 is almost 40% higher, but its mass-based lower heating value (LHV) is 2.8 times lower. Hence, more fuel must be injected to reach the same engine load as in a conventional gasoline engine, leading to an increased cooling effect.

Fast and exact measurement of engine oil consumption is a very difficult task. Our aim is to achieve this measurement at a common test bed without engine modifications. We resolved this problem with a new technique using Laser Mass Spectrometry to detect appropriate tracers in the raw engine exhaust. The tracers are added to the engine oil. to the engine oil. For detection of these tracers we use a Laser Mass Spectrometer (LAMS). This is a combination of resonant laser ionization (with an all-solid-state laser) and Time-of-Flight Mass Spectrometry. Currently this is the only way to detect oil originated molecules (like our tracers) in the raw exhaust very fast (50 Hz) and sensitive (ppb-region). Thus, engine mapping of oil consumption over load and speed can be performed in 1-2 days with about 90 measurements. Even measurement during dynamic engine operation is possible, but not quantitative (due to the lack of information about dynamic exhaust gas mass flow).

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

In recent years, there has been an impetus in the automotive industry to develop newer diesel injection systems with a view to reducing fuel consumption and emissions. This development has led to hardware capable of higher pressures, typically up to 2500 bar. An increase in pressure will result in a corresponding increase in fuel temperature after compression with studies showing changes in fuel temperatures of up to 150 °C in 1000-2500 bar injection systems. Until recently, the addition of Fatty Acid Methyl Esters, FAME, to diesel had been blamed for a number of fuel system durability issues such as injector deposits and fuel filter blocking. Despite a growing acceptance within the automotive and petrochemical industries that FAME is not solely to blame for diesel instability, there is a lack of published literature in the area, with many studies still focusing on FAME oxidation to explain deposit formation and hardware durability.

This paper describes the development and automated calibration of a compact analytically based model of the wall-wetting phenomenon of modern port fuel-injected (PFI) spark-ignition (SI) gasoline engines. The wall-wetting model, based on the physics of forced convection with phase change, is to be used in an automated model-based calibration program. The first stage of work was to develop a model of the wall-wetting phenomenon in Matlab. The model was then calibrated using experimental data collected from a 1.8-litre turbocharged I4 engine coupled to a dynamic 200kW AC dynamometer. The calibration was accomplished by adopting a two stage optimization approach. Firstly, a design of experiments (DoE) approach was used to establish the effect of the principal model parameters on a set of metrics that characterized the magnitude and duration of the measured lambda deviation during a transient.

This paper presents an object-oriented method customized for a tool-assisted development of car software components. Tough market conditions motivate smart software development. ASCET SD is a tool to generate target code from graphic specifications, avoiding costly programming in C. But ASCET lacks guidelines on what to do, how to do it, in what order, like a fully equipped kitchen without a cooking book. Plans to employ the tool for BMW vehicle software sparked off demand for an adequate, object-oriented real-time methodology. We show how to scan the methodology market in order to adopt an already existing method for this purpose. The result of the adaptation of a chosen method to ASCET SD is a pragmatic version of ROOM, which we call BROOM. We present a modeling guidebook that includes process recommendations not only for the automotive sector, but for real-time software development in general.

Vehicle start-stop systems are becoming increasingly prevalent on internal combustion engine (ICE) because of the capability to reduce emissions and fuel consumption in a cost effective manner. Thus, the ICE undergoes far more starting events, therefore, the behaviour of ICE during start-up becomes critical. In order to simulate and optimise the engine start, Model in the Loop (MiL) simulation approach was selected. A proceduralised cranking test has been carried out on a 2.0-liter turbocharged, gasoline direct injection (GDI) engine to collect data. The engine behaviour in the first 15 seconds was split into eight different phases and studied. The engine controller and the combustion system were highly transient and interactive. Thus, a controller model that can set accurate boundary conditions is needed. The relevant control functions of throttle opening and spark timing have been implemented in Matlab/Simulink to simulate the behaviours of the controller.