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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 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, modern engines feature increasingly 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.

Oxygenated fuels such as polyoxymethylene dimethyl ethers (OME) offer a chance to significantly decrease emissions while switching to renewable fuels. However, compared to conventional diesel fuel, they have lower heating values and different evaporation behaviors which lead to differences in spray, mixture formation as well as ignition delay. In order to determine the mixture formation characteristics and the combustion behavior of neat OME3-5, optical investigations have been carried out in a high-pressure-chamber using shadowgraphy, mie-scatterlight and OH-radiation recordings. Liquid penetration length, gaseous penetration length, lift off length, spray cone angle and ignition delay have been determined and compared to those measured with diesel-fuel over a variety of pressures, temperatures, rail pressures and injection durations.

UV-absorption measurements are sparse in diesel(-like) combustion, particularly close to the premixed burn. Thus, such measurements are conducted in diesel-like jets in a high-pressure vessel in this work, using 1D spontaneous Raman scattering (SRS) from N2. Stokes (~263 nm) and anti-Stokes (~235 nm) SRS induced by a krypton fluoride excimer (KrF*) laser (~248 nm) is exploited. Anti-Stokes SRS can be directly used for attenuation correction of laser-induced fluorescence (LIF) from NO at ~236 nm. Results show the importance of attenuation correction, although the jets are largely non-sooting. To identify absorbers, effects of SRS wavelength, measurement time in the injection event, location in the flame, jet width (JW), temperature, CO concentration, and injection pressure are considered. Particularly strong attenuation observed around the time of second-stage ignition appears to be primarily caused by combustion intermediates such as partially oxidized fuel.

The optimization study presented herein is aimed to minimize the fuel consumption and engine-out emissions using commercially available EN15940 compatible HVO (Hydrogenated Vegetable Oil) fuel. The investigations were carried out on FEV’s 3rd generation HECS (High Efficiency Combustion System) multi-cylinder engine (1.6L, 4 Cylinder, Euro 6). Using a global DOE approach, the effects of calibration parameters on efficiency and emissions were obtained and analyzed. This was followed by a global optimization procedure to obtain a dedicated calibration for HVO. The study was aiming for efficiency improvement and it was found that at lower loads, higher fractions of low pressure EGR in combination with lower fuel injection pressures were favorable. At higher loads, a combustion center advancement, increase of injection pressure and reduced pilot injection quantities were possible without exceeding the noise and NOx levels of the baseline Diesel.

The purpose of this study is to investigate the effect of spray-bowl interaction on combustion, and pollutants formation at one specific high-load point of a single-cylinder small-bore diesel engine through computational analysis. The simulations are performed using Representative Interactive Flamelet (RIF) model with detailed chemical kinetics. Detailed chemistry-based soot model is used for the prediction of soot emissions. The simulations are performed for five different injection timings. Model-predicted cylinder pressure and exhaust emissions are validated against the measured data for all the injection timings. A new method - Two-part analysis - is then applied to investigate the spray-bowl interaction. Two-part analysis splits the volume of the combustion chamber into two, namely the piston bowl and the squish volume. Through analysis, among others the histories of soot, carbon monoxide (CO) and nitric oxide (NO ) emissions inside both volumes are shown.

Engine processes are subject to cyclic fluctuations, which a have direct effect on the operating and emission behavior of the engine. The fluctuations in direct injection gasoline engines are induced and superimposed by the flow and the injection. In stratified operation they can cause serious operating problems, such as misfiring. The current state of knowledge on the formation and causes of cyclic fluctuations is rather limited, which can be attributed to the complex nature of flow instabilities. The current investigation analyzes the cyclic fluctuations of the in-cylinder charge motion and the mixture formation in a direct injection gasoline engine using laser-optical diagnostics and numerical 3D-calculation. Optical measurement techniques and pressure indication are used to measure flow, mixture formation, and combustion processes of the individual cycles.

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.

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 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 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.

Dielectric barrier discharge (DBD) offers the advantage to excite and dissociate molecules in the exhaust gas stream. Those dissociated and excited species are oxidizing or reducing harmful exhaust gas components. The advantage of a plasma chemical system in comparison to a catalytic measure for exhaust gas treatment is the instantaneous activity at ambient temperature from the starting of the engine. The investigations reviewed in this paper are dealing with the plasma chemical oxidation of hydrocarbons in the exhaust gas stream during cold start conditions. The article concerns the design and development of a plasma-system in order to decrease the hydrocarbon emissions from engine start till catalyst light off. Vehicle results in the New European Driving Cycle show a hydrocarbon conversion of more than 42% in the first 11 seconds from engine start. In this period nearly all types of hydrocarbon were reduced.