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Powertrain electrification is becoming increasingly common in the transportation sector to address the challenges of global warming and deteriorating air quality. This paper introduces a novel “Build Your Hybrid” approach to experience and test various hybrid powertrain concepts. This approach is applied to the light commercial vehicles (LCV) segment due to the attractive combination of a Diesel engine and a partly electrified powertrain. For this purpose, a demonstrator vehicle has been set up with a flexible P02 hybrid topology and a prototype Hybrid Control Unit (HCU). Based on user input, the HCU software modifies the control functions and simulation models to emulate different sub-topologies and levels of hybridization in the demonstrator vehicle. Three powertrain concepts are considered for LCVs: HV P2, 48V P2 and 48V P0 hybrid. Dedicated hybrid control strategies are developed to take full advantage of the synergies of the electrical system and reduce CO2 and NOx emissions.

New 12 V/48 V power net architectures are potential solutions to close the gap between customer needs and legislative requirements. In order to exploit their potential, an increased effort is needed for functional implementation and hardware integration. Shifting of development tasks to earlier phases (frontloading) is a promising solution to streamline the development process and to increase the maturity level at early stages. This study shows the potential of the frontloading of development tasks by implementing a virtual 48 V mild hybridization in an engine-in-the-loop (EiL) setup. Advanced simulation technics like functional mock-up interface- (FMI) based co-simulation are utilized for the seamless integration of the real-time (RT) simulation models and allow a modular simulation framework as well as a decrease in development time.

The low temperature combustion concept is very attractive for reducing NOx and soot emissions in diesel engines. However, it has potential limitations due to higher combustion noise, CO and HC emissions. A multiple injection strategy is an effective way to reduce unburned emissions and noise in LTC. In this paper, the effect of multiple injection strategies was investigated to reduce combustion noise and unburned emissions in LTC conditions. A hybrid surrogate fuel model was developed and validated, and was used to improve LTC predictions. Triple injection strategies were considered to find the role of each pulse and then optimized. The split ratio of the 1st and 2nd pulses fuel was found to determine the ignition delay. Increasing mass of the 1st pulse reduced unburned emissions and an increase of the 3rd pulse fuel amount reduced noise. It is concluded that the pulse distribution can be used as a control factor for emissions and noise.

Lean burn gasoline spark-ignition engines can support the reduction of CO2 emissions for future hybrid passenger cars. Very high efficiencies and very low NOx raw emissions can be achieved, if relative air/fuel ratios λ of 2 and above can be reached. The biggest challenge here is to assure a reliable ignition process and to enhance the fuel oxidation in order to achieve a short burn duration and a good combustion stability. This article aims at introducing an innovative combustion system fully optimized for ultra-lean operation and very high efficiency. Thereto, a new cylinder head concept has been realized with high peak firing pressure capability and with a low surface-to-volume ratio at high compression ratios. 1D and 3D simulations have been performed to optimize the compression ratio, charge motion and intake valve lift. Numerical calculations also supported the development of the ignition system.

Physics-based models in a closed-loop feedback control of a premixed charge compression ignition (PCCI) engine can improve the combustion efficiency and potentially reduce harmful NOx and soot emissions. A stand-alone multi-zone combustion model has been proposed in the literature using a physics-based mixing approach. The scalar dissipation rate emerged as the determining parameter in the model for mixing among different zones in the mixture fraction space. However, the calculation of the scalar dissipation rate depends on three approaches: three-dimensional computational fluid dynamics (3-D CFD) combustion simulations based on representative interactive flamelet (RIF) model, tabulation, or an empirical algebraic model of the scalar dissipation rate fitted for the given operating conditions of the engine. While the 3-D CFD approach provides accurate results, it is computationally too expensive to use the multi-zone model in closed-loop control.

New combustion processes require an understanding of the highly three-dimensional flow field to effectively decrease fuel consumption and pollutant emission. Due to the complex spatial character of the flow the knowledge of the development of the flow in an extended volume is necessary. Previous investigations were able to visualize the discrete three-dimensional flow field through multi-plane stereoscopic PIV. In this study, cycle resolved tomographic particle-image velocimetry measurement have been performed to obtain a fully resolved representation of the three-dimensional flow structures at each instant. The analysis is based on the measurements at 80°, 160°, and 240° after top dead center(atdc) such that the velocity distributions at the intake, the end of the intake, and the compression stroke at an engine speed of 1,500 rpm are discussed in detail.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

Experimental studies have been undertaken on a single-cylinder HPCR diesel engine with a compression ratio of 15.5:1 to explore the effect of fuel injection strategy on cycle by cycle stability. The influence of the number, separation and quantity of pilot injections on the coefficient of variation of IMEP has been investigated at -20°C, 1000 rev/min, post-start idling conditions. Injection strategy and glow plug temperature trade-off has also been investigated at a range of soak temperatures. Up to four pilot injections have been used. For timing of the main injection near to the optimum, CoVIMEP values of 10% or better can be achieved. Closer spacing of injections improved stability and extended the range of timings to meet target stability. The best combinations of pilot number and pilot quantity varied with total fuel delivered.

The effect of compression ratio on sensitivity to changes in start of injection and air-fuel ratio has been investigated on a single-cylinder DI diesel engine at fixed low and medium speeds and loads. Compression ratio was set to 17.9:1 or 13.7:1 by using pistons with different bowl sizes. Injection timing and air-to-fuel ratio were swept around a nominal map point at which gross IMEP and NOx values were matched for the two compression ratios. It was found that CO, HC and ISFC were higher at low compression ratio, but the soot/NOx trade-off improved and this could be exploited to reduce the fuel economy penalty. Sensitivity to inputs is generally similar, but high compression ratio tended to have steeper response gradients. Reducing compression ratio to 13.7 gave rise to a marked degradation of performance at light load, producing high CO emissions and a fall in combustion efficiency. This could be eased by reducing rail pressure, but the advantage in smoke emission was lost.

An operational scheme with fuel-lean and exhaust gas dilution in spark-ignited engines increases thermal efficiency and decreases NOx emission, while these operations inherently induce combustion instability and thus large cycle-to-cycle variation in engine. In order to stabilize combustion variations, the development of an advanced ignition system is becoming critical. To quantify the impact of spark-ignition discharge, ignitability tests were conducted in an optically accessible combustion vessel to characterize the flame kernel development of lean methane-air mixture with CO₂ simulating exhaust diluent. A shrouded fan was used to generate turbulence in the vicinity of J-gap spark plug and a Variable Output Ignition System (VOIS) capable of producing a varied set of spark discharge patterns was developed and used as an ignition source. The main feature of the VOIS is to vary the secondary current during glow discharge including naturally decaying and truncated with multiple strikes.

A Ford 2.4-liter 115PS light-duty diesel engine was modified to allow solenoid control of the oil feed to the piston cooling jets, enabling these to be switched on or off on demand. The influence of the jets on piston temperatures, engine thermal state, gaseous emissions and fuel economy has been investigated. With the jets switched off, piston temperatures were measured to be between 23 and 88°C higher. Across a range of speed-load points, switching off the jets increased engine-out emissions of NOx typically by 3%, and reduced emissions of CO by 5-10%. Changes in HC were of the same order and were reductions at most conditions. Fuel consumption increased at low-speed, high-load conditions and decreased at high-speed, low-load conditions. Applying the results to the NEDC drive cycle suggests active on/off control of the jets could reduce engine-out emissions of CO by 6%, at the expense of a 1% increase in NOx, compared to the case when the jets are on continuously.

The U.S. Environmental Protection Agency (EPA) contracted with FEV, Inc. to estimate the per-vehicle cost of employing selected advanced efficiency-improving technologies in light-duty motor vehicles. The development of transparent, reliable cost analyses that are accessible to all interested stakeholders has played a crucial role in establishing feasible and cost effective standards to improve fuel economy and reduce greenhouse gas (GHG) emissions. The FEV team, together with engineering staff from EPA's National Vehicle and Fuel Emissions Laboratory, and FEV's subcontractor, Munro & Associates, developed a robust costing methodology based on tearing down, to the piece part level, relevant systems, sub-systems, and assemblies from vehicles “with and without” the technologies being evaluated.

Fuels derived from biomass will most likely contain oxygen due to the high amount of hydrogen needed to remove oxygen in the production process. Today, alcohol fuels (e. g. ethanol) are well understood for spark ignition engines. The Institute for Combustion Engines at RWTH Aachen University carried out a fuel investigation program to explore the potential of alcohol fuels as candidates for future compression ignition engines to reduce engine-out emissions while maintaining engine efficiency and an acceptable noise level. The soot formation and oxidation process when using alcohol fuels in diesel engines is not yet sufficiently understood. Depending on the chain length, alcohol fuels vary in cetane number and boiling temperature. Decanol possesses a diesel-like cetane number and a boiling point in the range of the diesel boiling curve. Thus, decanol was selected as an alcohol representative to investigate the influence of the oxygen content of an alcohol on the combustion performance.

Diesel fuels are complex mixtures of thousands of hydrocarbons. Since modeling their combustion characteristics with the inclusion of all hydrocarbon species is not feasible, a hybrid surrogate model approach is used in the present work to represent the physical and chemical properties of three different diesel fuels by using up to 13 and 4 separate hydrocarbon species, respectively. The surrogates are arrived at by matching their distillation profiles and important properties with the real fuel, while the chemistry surrogates are arrived at by using a Group Chemistry Representation (GCR) method wherein the hydrocarbon species in the physical property surrogates are grouped based on their chemical classes, and the chemistry of each class is represented by using up to two hydrocarbon species.

A great deal of current automotive engineering effort involves the development of three-way catalyst-based emission control systems that seek to minimize fuel consumption while simultaneously meeting stringent exhaust emission standards. Mitigation of emissions is enhanced in a three-way catalyst system when the system air-fuel ratio (A/F) is in proximity to ideal burning or stoichiometry. This paper is concerned with extending methods used for determining engine calibrations to closed-loop systems with three-way catalysts. The paper presents a simulation model that employs experimentally obtained data to characterize the A/F control loop.

Targeted fuel blending is a known method to improve the performance of an automotive engine. Two candidates for a biofuel blend are the linear C8H18O isomers 1-octanol and di-n-butyl ether (DNBE). Both fuels feature an increased amount of oxygen that reduces soot emissions. However, physical properties of both fuels differ significantly and thus, a different type of spray mixing and combustion is expected: The low reactivity of 1-octanol causes a long ignition delay enabling a better mixture homogenization, but also causes HC and CO emissions. DNBE in contrary is highly volatile, has a short ignition time and thus can act as an ignition booster for 1-octanol without losing positive effects concerning emissions. In this work a spray study is performed for blends of 1-octanol and DNBE. Measurements are conducted under diesel-like engine conditions with an 8-hole piezo injector. High-speed Schlieren and Mie scattering techniques are used for spray visualizations.

The noise performance of fully electric vehicles is essential to ensure that they gain market acceptance. This can be a challenge for several reasons. Firstly, there is no masking from the internal combustion engine. Next, there is pressure to move to cost-efficient motor designs such as Switched Reluctance Motors, which have worse vibro-acoustic behaviour than their Permanent Magnet counterparts. Finally, power-dense, higher speed motors run closer fundamental frequency to the structural resonances of the system [1]. Experience has shown that this challenge is frequently not met. Reputable suppliers have designed and developed their “quiet” subsystems to state of the art levels, only to discover that the assembled E-powertrain is unacceptably noisy. The paper describes the process and arising results for the noise simulation of the complete powertrain.

Besides optical and haptic criteria, the interior noise especially influences the quality impression of a vehicle. Separately audible disturbing noises are usually perceived as inadequate product quality. As a result, the reduction of disturbing noise components is a key factor for the overall product quality. Since the acoustic optimization is a complex and time consuming process, the need for an analysis tool which identifies automatically disturbing engine noise components within the vehicle interior noise is high. For this reason, a novel analysis tool has been developed which extracts tonal and impulsive engine noise components from the overall engine noise, and evaluates the annoyance of each noticeable engine component automatically. In addition, each disturbing noise is allocated to the emitting engine component. It is then possible to listen to each engine component noise individually and synthesize a target noise by superimposing manually weighted component noises.

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.

Stringent global emissions legislation demands effective NOx reduction strategies, particularly for the aftertreatment, and current typical liquid urea SCR systems achieve efficiencies greater than 90% [1]. However, with such high-performing systems comes the trade-off of requiring a tank of reductant (urea water solution) to be filled regularly, usually as soon as the fuel fillings or as far as oil changes. Advantages of solid reductants, particularly ammonium carbamate, include greater ammonia densities, enabling the reductant refill interval to be extended several multiples versus a given reductant volume of urea, or diesel exhaust fluid (DEF) [2]. An additional advantage is direct gaseous ammonia dosing, enabling reductant injection at lower exhaust temperatures to widen its operational coverage achieving greater emissions reduction potential [3], as well as eliminating deposits, reducing mixing lengths, and avoiding freeze/thaw risks and investments.