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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.

A fuel cell based Auxiliary Power Unit (APU) represents a rather complex technical system consisting of different subsystems, components and low-level controllers. Particularly in the case of gasoline-fueled systems, a sophisticated supervisory control is needed to manage the sequential control and to achieve fault tolerant and fail-safe operation. In this paper, a state machine-based APU control concept is presented, offering a transparent and modular structure. In addition to a superior control system (top level supervisor) that manages the overall strategies and the interaction of all subsystems, each subsystem is equipped with its own subsystem control (second level supervisor). This controller is responsible for all subsystem specific issues. The APU control concept was implemented using Matlab®/Simulink® and applied on a rapid prototyping controller unit.

This paper focuses on start-up technology for fuel processing systems with special emphasis on gasoline fueled burners. Initially two different fuel processing systems, an autothermal reformer with preferential oxidation and a steam reformer with membrane, are introduced and their possible starting strategies are discussed. Energy consumption for preheating up to light-off temperature and the start-up time is estimated. Subsequently electrical preheating is compared with start-up burners and the different types of heat generation are rated with respect to the requirements on start-up systems. Preheating power for fuel cell propulsion systems necessarily reaches up to the magnitude of the electrical fuel cell power output. A gasoline fueled burner with thermal combustion has been build-up, which covers the required preheating power.

Exhaust aftertreatment systems must function sufficiently over the full useful life of a vehicle. In Europe this is currently defined as 160.000 km. With the introduction of Euro 7 it is expected that the required mileage will be extended to 240.000 km. This will then be consistent with the US legislation. In order to quantify the emission impact of exhaust system degradation, an Euro 7 exhaust aftertreatment system is aged by different accelerated approaches: application of the Standard Bench Cycle, the ZDAKW cycle, a novel ash loading method and borderline aging. The results depict the impact of oil ash on the oxygen storage capacity. For tailpipe emissions, the maximum peak temperatures are the dominant aging factor. The cold start performance is effected by both, thermal degradation and ash accumulation. An evaluation of this emission increase requires appropriate benchmarks.

Pilot injection (PI) during the negative-valve-overlap (NVO) period is one method to improve control of combustion in gasoline controlled auto-ignition engines. This is generally attributed to both chemical and thermal effects. However, there are little experimental data on active species formed by the combusting PI and their effect on main combustion in real engines. Thus, it is the objective of the current study to apply and assess optical in-cylinder diagnostics for these species. Firstly, the occurrence and nature of combustion during the NVO period is investigated by spectrally-resolved multi-species flame luminescence measurements. OH*, CH*, HCO*, CO-continuum chemiluminescence, and soot luminosity are recorded. Secondly, spectrally-, spatially-, and cycle-resolved laser-induced fluorescence measurements of formaldehyde are conducted. It is attempted to find a cycle-resolved measure of the chemical effect of PI.

In this paper a new approach is presented to evaluate the combustion behaviour of homogeneous gasoline engines by predicting burn delay and -duration in a way which can be obtained under the time constraints of the development process. This is accomplished by means of pure in-cylinder flow simulations without a classical combustion model. The burn delay model is based on the local distribution of the turbulent flow near the spark plug. It features also a methodology to compare different designs regarding combustion stability. The correlation for burn duration uses a turbulent characteristic number that is obtained from the turbulent flow in the combustion chamber together with a model for the turbulent burning velocity. The results show good agreement with the combustion process of the analyzed engines.

To achieve global market and brand specific drivability characteristics as unique selling proposition for the increasing number of passenger car derivatives, an objectified evaluation approach for the drivability capabilities of the various cars is required. Thereto, it is necessary to evaluate the influence of different engine concepts in various complex and interlinked powertrain topologies during engine load change maneuvers based on physical criteria. Such an objectification approach enables frontloading of drivability related engineering tasks by the execution of drivability development and calibration work within vehicle subcomponent-specific closed-loop real-time co-simulation environments in early phases of a vehicle development program. So far, drivability functionalities could be developed and calibrated only towards the end of a vehicle development program, when test vehicles with a sufficient level of product maturity became available.

Maintaining low NOx emissions over the operating range of diesel engines continues to be a major issue. However, optical measurements of nitric oxide (NO) are lacking particularly in the core of diesel jets, i.e. in the region of premixed combustion close to the spray axis. This is basically caused by severe attenuation of both the laser light and fluorescent emission in laser-induced fluorescence (LIF) applications. Light extinction is reduced by keeping absorption path lengths relatively short in this work, by investigating diesel jets in a combustion vessel instead of an engine. Furthermore, the NO-detection threshold is improved by conducting 1-d line measurements instead of 2-d imaging. The NO-LIF data are corrected for light attenuation by combined LIF and spontaneous Raman scattering. The quantified maximum light attenuation is significantly lower than in comparable previous works, and its wavelength dependence is surprisingly weak.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, these engines feature an increasing number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

In this work, a transport and mixing model that calculates mixing in thermodynamic phase space was derived and validated. The mixing in thermodynamic multizone space is consistent to the one in the spatially resolved physical space. The model is developed using a turbulent channel flow as simplified domain. This physical domain of a direct numerical simulation (DNS) is divided into zones based on the quantitative value of transported scalars. Fluxes between the zones are introduced to describe mixing from the transport equation of the probability density function based on the mixing process in physical space. The mixing process of further scalars can then be carried out with these fluxes instead of solving additional transport equations. The relationship between the exchange flux in phase space and the concept of scalar dissipation are shown and validated by comparison to DNS results.

The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.

The demand for lower CO2 emissions requires not just the optimization of every single component but the complete system. For a transmission system, it is important to optimize the transmission hardware as we well as the interaction of powertrain components. For automatic transmission with wide ratio spreads, the main losses are caused by the actuation system, which can be reduced with use of ondemand actuation systems. In this paper, a new on-demand electromechanical actuation system with validation results on a clutch test bench is presented. The electro-mechanical actuator shows an increase in the efficiency of 4.1 % compared to the conventional hydraulic actuation in a simulated NEDC (New European Driving Cycle) cycle. This increase is based on the powerless end positions of the actuator (engaged and disengaged clutch). The thermal tension and wear are compensated with a disk spring. This allows a stable control over service life.

A main focus of development in modern SI engine technology is variable valve timing, which implies a high potential of improvement regarding fuel consumption and emissions. Variable opening, period and lift of inlet and outlet valves enable numerous possibilities to alter gas exchange and combustion. However, this additional variability generates special demands on the calibration process of specific engine control devices, particularly under cold start and warm-up conditions. This paper presents procedures, based on Hardware-in-the-Loop (HiL) simulation, to support the classical calibration task efficiently. An existing approach is extended, such that a virtual combustion engine is available including additional valve timing variability. Engine models based purely on physical first principles are often not capable of real time execution. However, the definition of initial parameters for the ECU requires a model with both real time capability and sufficient accuracy.

The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.

Cyclic fluctuations of the in-cylinder processes in a Direct Injection Spark Ignition (DISI) engine may strongly affect the engine operation causing misfires or variations in the indicated mean effective pressure (imep). Particularly misfires prevent compliance with current or future exhaust emission legislations. Nevertheless, the origin of cyclic fluctuations is not well understood since fluctuations of in-cylinder air flow, fuel injection and wall interaction have to be considered. This paper focusses on a detailed experimental analysis of the origin of cyclic fluctuations in a DISI engine with an air guided combustion process by means of advanced Laser Induced Exciplex Fluorescence (LIEF) measurements. It reveals that cycle-to-cycle variations primarily originate from the air/fuel ratio at the spark plug.

An improved plasma reactor has been designed, built and evaluated. It is characterized by a reduced power per area ratio, relative to previous designs, and includes several improvements to run the whole system safely in a car. The new reactor design includes a concentric inner high voltage electrode, a grounded outer electrode, a shielded high-voltage and high temperature resistant electrical connection. A generator controller has been developed for better control of operating conditions as required during the engine cold start phase. The new generator/reactor system was installed in the exhaust pipe of a gasoline direct injection engine. HC emissions could be reduced up to 30 % in the first 40 seconds of a cold start test. In addition to HC treatment the dielectric barrier discharge has also been investigated as a method for regenerating a diesel particulate trap.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

The efficient operation of powertrain test benches in research and development is strongly influenced by the state of “health” of the functional test object. Hence, the use of Early Damage Detection Systems (EDDS) with Unit Under Test (UUT) monitoring is becoming increasingly popular. An EDDS should primarily avoid total loss of the test object and ensure that damaged parts are not completely destroyed, and can still be inspected. Therefore, any abnormality from the standard test object behavior, such as an exceeding of predefined limits, must be recognized at an early testing time, and must lead to a shutdown of the test bench operation. With sensors mounted on the test object, it is possible to isolate the damage cause in the event of its detection. Advanced EDDS configurations also optimize the predefined limits by learning new shutdown values according to the test object behavior within a very short time.

Depending on a vehicles drive cycle, an improvement of the overall drivetrain efficiency does not necessarily have to go along with an improvement of its mileage. In here the ratio of energy to overcome rolling resistance, aerodynamic drag, acceleration and energy wasted directly in wheel brakes is responsible for potentially differing trends. A detailed knowledge of energy flows, sources and sinks makes up a substantial step into optimizing any drive train. Most fuel energy leaves the drivetrain via exhaust pipes. Next to usable mechanical energy, a big amount is spent to heat up the system directly or to overcome drive train friction, which is converted into heat to warm up the system additionally. An in depth quantification of the most important energy flows for an upper middle-sized class gasoline powered drive train is given as results of warm-up cycle simulations.

The presented paper deals with the set-up and performance of a newly developed control system as well as with achieved engine results. This control system is able to control the entire cylinder pressure trace by using a flexible rate shaping injector and iterative learning control (ILC). Standard thermodynamic cycles, like isobaric and Seiliger cycles, and a newly suggested class of cycles are generated and analyzed on a single cylinder engine. With this control system an extremely flexible tool for optimization of combustion processes is available to exploit the full potential of injection rate- shaping on diesel engines.