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A comparative study on engine performance and emissions (NOx, soot) formation has been carried out for the Volvo D12C diesel engine fueled by Rapeseed Methyl Ester, RME and conventional diesel oil. The fuel and combustion models used in this paper are the modifications of those described in [1–3]. The numerical results for different load cases illustrate that for both fuels nearly 100% combustion efficiency was predicted; in the case of RME, the cumulative heat release was compared with the RME LHV, 37.2 kJ/g. To minimize soot and NOx emissions, 25–30% EGR levels depending on the engine loads and different injection timings were analyses. To illustrate the optimal engine performance conditions, a special technique based on the time-transient parametric ϕ-T maps [4] has been used.

When increasing EGR from low levels to a level that corresponds to low temperature combustion, soot emissions initially increase due to lower soot oxidation before decreasing to almost zero due to very low soot formation. At the EGR level where soot emissions start to increase, the NOx emissions are low, but not sufficiently low to comply with future emission standards and at the EGR level where low temperature combustion occurs CO and HC emissions are too high. The purpose of this study was to investigate the possibilities for shifting the so-called soot bump (where soot levels are increased) to higher EGR levels, or to reduce the magnitude of the soot bump using very high injection pressures (up to 240 MPa) while reducing the NOx emissions using EGR. The possibility of reducing the CO and HC emissions at high EGR levels due to the increased mixing caused by higher injection pressure was also investigated and the flame was visualized using an endoscope at chosen EGR values.

Predicting fuel economy during early stages of concept development or feasibility study for a new type of powertrain configuration is an important key factor that might affect the powertrain configuration decision to meet CAFE standards. In this paper an efficient model has been built in order to evaluate the fuel economy for a new type of charge sustaining series hybrid vehicle that uses a Genset assembly (small 2 cylinders CNG fueled engine coupled with a generator). A first order mathematical model for a Li-Ion polymer battery is presented based on actual charging /discharging datasheet. Since the Genset performance data is not available, normalized engine variables method is used to create powertrain performance maps. An Equivalent Consumption Minimization Strategy (ECMS) has been implemented to determine how much power is supplied to the electric motor from the battery and the Genset.

Fuel consumption of vehicles has received increased attention in recent years; however one neglected area that can have a large effect on this is the energy usage for the interior climate. This study aims to investigate the energy usage for the interior climate for different conditions by measurements on a complete vehicle. Twelve different NEDC tests in different temperatures and thermal states of the vehicle were completed in a climatic wind tunnel. Furthermore one temperature sweep from 43° to −18°C was also performed. The measurements focused on the heat flow of the air, from its sources, to its sink, i.e. compartment. In addition the electrical and mechanical loads of the climate system were included. The different sources of heating and cooling were, for the tested powertrain, waste heat from the engine, a fuel operated heater, heat pickup of the air, evaporator cooling and cooling from recirculation.

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. Operating a practical engine with SACI combustion is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. This work describes the process and results of developing a fast-running control-oriented model for the autoignition phase of SACI combustion. A data-driven model is selected, specifically artificial neural networks (ANNs).

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

Squeak and rattle (S&R) are nonstationary annoying and unwanted noises in the car cabin that result in considerable warranty costs for car manufacturers. Introduction of cars with remarkably lower background noises and the recent emphasis on electrification and autonomous driving further stress the need for producing squeak- and rattle-free cars. Automotive manufacturers use several road disturbances for physical evaluation and verification of S&R. The excitation signals collected from these road profiles are also employed in subsystem shaker rigs and virtual simulations that are gradually replacing physical complete vehicle test and verification. Considering the need for a shorter lead time and the introduction of optimisation loops, it is necessary to have efficient and inclusive excitation load cases for robust S&R evaluation.

In the U.S., the teenage driving population is at the highest risk of being involved in a crash. Teens often demonstrate poor vehicle control skills and poor ability to identify hazards, thus proper understanding of automotive indicators and warnings may be even more critical for this population. This research evaluates teen drivers’, between 15 to 17 years of age, understanding of symbols from vehicles featuring advanced driving assistant systems and multiple powertrain configurations. Teen drivers’ (N=72) understanding of automotive symbols was compared to three other groups with specialized driving experience and technical knowledge: automotive engineering graduate students (N=48), driver rehabilitation specialists (N=16), and performance driving instructors (N=15). Participants matched 42 symbols to their descriptions and then selected the five symbols they considered most important.

Axial cooling fans are commonly used in electric vehicles to cool batteries with high heating load. One drawback of the cooling fans is the high aeroacoustic noise level resulting from the fan blades and the obstacles facing the airflow. To create a comfortable cabin environment in the vehicle, and to reduce exterior noise emission, a low-noise installation design of the axial fan is required. The purpose of the study is to investigate efficient computational aeroacoustics (CAA) simulation processes to assist the cooling-fan installation design. In this paper we report the current progress of the investigation, where the narrow-band components of the fan noise is focused on. Two methods are used to compute the noise source. In the first method the source is computed from the flow field obtained using the unsteady Reynolds-averaged Navier-Stokes equations (unsteady RANS, or URANS) model.

To characterize the effects of renewable fuels on particulate emissions from GDI engines, engine experiments were conducted using EN228-compliant gasoline fuel blends containing no oxygenates, 10% ethanol (EtOH), or 22% ethyl tert-butyl ether (ETBE). The experiments were conducted in a single cylinder GDI engine using a 6-hole fuel injector operated at 200 bar injection pressure. Both PN in raw exhaust and solid PN (SPN) were measured at two load points and various start of injection (SOI) timings. Raw PN and SPN results were classified into various size ranges, corresponding to current and future legislations. At early SOI timings, where particulate formation is dominated by diffusion flames on the piston due to liquid film, the oxygenated blends yielded dramatically higher PN and SPN emissions than reference gasoline because of fuel effects.

The use of Low Pressure - Exhaust Gas Recirculation (EGR) is intended to allow displacement reduction in turbocharged gasoline engines and improve fuel economy. Low Pressure EGR designs have an advantage over High Pressure configurations since they interfere less with turbocharger efficiency and improve the uniformity of air-EGR mixing in the engine. In this research, Low Pressure (LP) cooled EGR is evaluated on a turbocharged direct injection gasoline engine with variable valve timing using both simulation and experimental results. First, a model-based calibration study is conducted using simulation tools to identify fuel efficiency gains of LP EGR over the base calibration. The main sources of the efficiency improvement are then quantified individually, focusing on part-load de-throttling of the engine, heat loss reduction, knock mitigation as well as decreased high-load fuel enrichment through exhaust temperature reduction.

The pursuit of greater fuel economy in internal combustion engines requires the optimization of all subsystems including thermal management. The reduction of cooling power required by the electromechanical coolant pump, radiator fan(s), and thermal valve demands real time control strategies. To maintain the engine temperature within prescribed limits for different operating conditions, the continual estimation of the heat removal needs and the synergistic operation of the cooling system components must be accomplished. The reductions in thermal management power consumption can be achieved by avoiding unnecessary overcooling efforts which are often accommodated by extreme thermostat valve positions. In this paper, an optimal nonlinear controller for a military M-ATV engine cooling system will be presented. The prescribed engine coolant temperature will be tracked while minimizing the pump, fan(s), and valve power usage.

The advancements in automation, big data computing and high bandwidth networking has expedited the realization of Industrial Internet of Things (IIoT). IIoT has made inroads into many sectors including automotive, semiconductors, electronics, etc. Particularly, it has created numerous opportunities in the automotive manufacturing sector to realize the new aura of platform concepts such as smart material flow control. This paper provides a thought provoking application of IIoT in automotive composites body shop. By creating a digital twin for every physical part, we no longer need to adhere to the conventional manufacturing processes and layouts, thus opening up new opportunities in terms of equipment and space utilization. The century-old philosophy of the assembly line might not be the best layout for vehicle manufacturing, thus proposing a novel assembly grid layout inspired from a colony of ants working to accomplish a common goal.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Combustion phasing of Spark Ignition (SI) engines is traditionally regulated with map-based spark timing (SPKT) control. The calibration time and effort of this feed forward SPKT control strategy becomes less favorable as the number of engine control actuators increases. This paper proposes a model based combustion phasing control frame work. The feed forward control law is obtained by real time numerical optimization utilizing a high-fidelity combustion model that is based on flame entrainment theory. An optimization routine identifies the SPKT which phases the combustion close to the target without violating combustion constraints of knock and excessive cycle-by-cycle covariance of indicated mean effective pressure (COV of IMEP). Cylinder pressure sensors are utilized to enable feedback control of combustion phasing. An Extended Kalman Filter (EKF) is applied to reject sensor noise and combustion variation from the cylinder pressure signal.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

Emissions of nitrogen oxides (NOx) from heavy-duty diesel engines are subject to more stringent environmental legislation. Selective catalytic reduction (SCR) over metal ion-exchanged zeolites is in this connection an efficient method to reduce NOx. Understanding durability of the SCR catalyst is crucial for correct design of the aftertreatment system. In the present paper, thermal and chemical ageing of Fe-BEA as NH3-SCR catalyst is studied. Experimental results of hydrothermal ageing, and chemical ageing due to phosphorous and potassium exposure are presented. The catalyst is characterized by flow reactor experiments, nitrogen physisorption, DRIFTS, XRD, and XPS. Based on the experimental results, a multisite kinetic model is developed to describe the activity of the fresh Fe-BEA catalyst.

The current trend toward more fuel efficient vehicles with lower emission levels has prompted development of new combustion techniques for use in gasoline engines. Stratified combustion has been shown to be a promising approach for increasing the fuel efficiency. However, this technique is hampered by drawbacks such as increased particulate and standard emissions. This study attempts to address the issues of increased emission levels by investigating the influence of high frequency ionizing ignition systems, 350 bar fuel injection pressure and various tumble levels on particulate emissions and combustion characteristics in an optical SGDI engine operated in stratified mode on isooctane. Tests were performed at one engine load of 2.63 bar BMEP and speed of 1200 rpm. Combustion was recorded with two high speed color cameras from bottom and side views using optical filters for OH and soot luminescence.

Even though substantial improvements have been made for the lean NOx trap (LNT) catalyst in recent years, the durability still remains problematic because of the sulfur poisoning and sintering of the precious metals at high operating temperatures. Hence, commercial LNT catalysts were aged and tested in order to investigate their performance and activity degradation compared to the fresh catalyst, and establish a proper correlation between the aging methods used. The target of this study is to provide useful information for regeneration strategies and optimize the catalyst management for better performance and durability. With this goal in mind, two different aging procedures were implemented in this investigation. A catalyst was vehicle-aged in the vehicle chassis dynamometer for 100000 km, thus exposed to real conditions. Whereas, an accelerated aging method was used by subjecting a fresh LNT catalyst at 800 °C for 24 hours in an oven under controlled conditions.

This paper argues the importance of engineering heuristics and introduces an educational data-driven tool to help novice engineers develop their engineering heuristics more effectively. The main objective in engineering practice is to identify opportunities for improvement and apply methods to effect change. Engineers do so by applying ‘how to’ knowledge to make decisions and take actions. This ‘how to’ knowledge is encoded in engineering heuristics. In this paper, we describe a tool that aims to provide heuristic knowledge to users by giving them insight into heuristics applied by experts in similar situations. A repository of automotive data is transformed into a tool with powerful search and data visualization functionalities. The tool can be used to educate novice automotive engineers alongside the current resource intensive practices of teaching engineering heuristics through social methods such as an apprenticeship.