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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.

The societies around the world remain far from meeting the agreed primary goal outlined under the 2015 Paris Agreement on climate change: reducing greenhouse gas (GHG) emissions to keep global average temperature rise to well below 20°C by 2100 and making every effort to stay underneath of a 1.5°C elevation. Current emissions are rebounding from a brief decline during the economic downturn related to the Covid-19 pandemic. To get back on track to support the realization of the goal of the Paris Agreement, research suggests that GHG emissions should be roughly halved by 2030 on a trajectory to reach net zero by around mid-century.2 Although these are averaged global targets, every sector and country or market can and must contribute, especially higher-income and more developed countries bear the greater capacity to act. In 2020 direct tailpipe emissions from transport represented around 8 GtC02e, or nearly 15% of total emissions.

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.

Future Diesel engines must meet extended requirements regarding air-fuel ratio, exhaust gas recirculation (EGR) capability, and tailored exhaust gas temperatures in the complete engine map to comply with the future pollutant emission standards. In this respect, parallel turbines combined with two separate exhaust manifolds have the potential to increase the exhaust gas temperature upstream of the exhaust aftertreatment system and reduce the catalyst light-off time. Furthermore, variable exhaust valve (EV) lifts enable new control strategies of the boosting system without additional actuators. Therefore, hardware robustness can be improved. This article focuses on the parallel-sequential boosting concept (PSBC) for a high-performance four-cylinder Diesel engine with separated exhaust manifolds combined with EV deactivation. One EV per cylinder is connected to one of the separated exhaust manifolds and, thus, connected to one of the turbines.

In the field of gasoline powertrain calibration, the challenges are growing due to ever shorter time-to-market requirements and a simultaneous increase in powertrain complexity. In addition, the great variety of vehicle variants requires an increasing number of prototypes for calibration and validation tasks within the framework of the current Real Driving Emissions (RDE) regulations and the expected Post Euro 6 emission standards. Hardware-in-the-Loop (HiL) simulations have been introduced successfully to support the calibration tasks in parallel to the conventional vehicle development activities. The HiL approach enables a more reliable compliance with emission limits and improves the quality of calibrations, while reducing the number of prototype vehicles, test resources and thus overall development costs.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

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.

The growing number of passenger car variants and derivatives in all global markets, their high degree of software differentiability caused by regionally different legislative regulations, as well as pronounced market-specific customer expectations require a continuous optimization of the entire vehicle development process. In addition, ever stricter emission standards lead to a considerable increase in powertrain hardware and control complexity. Also, efforts to achieve market and brand specific multistep adjustable drivability characteristics as unique selling proposition, rapidly extend the scope for calibration and testing tasks during the development of powertrain control units. The resulting extent of interdependencies between the drivability calibration and other development and calibration tasks requires frontloading of development tasks.

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.

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.

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.

Due to experimental challenges, combustion of diesel-like jets has rarely been characterized by laser-based quantitative multiscalar measurements. In this work, recently developed laser diagnostics for combustion temperature and the concentrations of CO, O2, and NO are applied to a diesel-like jet, using a highly oxygenated fuel. The diagnostic is based on spontaneous Raman scattering (SRS) and laser-induced fluorescence (LIF) methods. Line imaging yields multiscalar profiles across the jet cross section. Measurements turn out to be particularly accurate, because near-stoichiometric combustion occurs in the central region of the jet. Thereby, experimental cross-influences by light attenuation and interfering emissions are greatly reduced compared to the combustion of conventional, sooting diesel fuel jets. This is achieved by fuel oxygenation and enhanced premixing.

In order to reduce engine out CO2 emissions, it is a main subject to find new alternative fuels from renewable sources. For identifying the specification of an optimized fuel for engine combustion, it is essential to understand the details of combustion and pollutant formation. For obtaining a better understanding of the flame behavior, dynamic structure large eddy simulations are a method of choice. In the investigation presented in this paper, an n-heptane spray flame is simulated under engine relevant conditions starting at a pressure of 50 bar and a temperature of 800 K. Measurements are conducted at a high-pressure vessel with the same conditions. Liquid penetration length is measured with Mie-Scatterlight, gaseous penetration length with Shadowgraphy and lift-off length as well as ignition delay with OH*-Radiation. In addition to these global high-speed measurement techniques, detailed spectroscopic laser measurements are conducted at the n-heptane flame.

Using a holistic 1D engine simulation approach for the modelling of full-transient engine operation, allows analyzing future engine concepts, including its exhaust gas aftertreatment technology, early in the development process. Thus, this approach enables the investigation of both important fields - the thermodynamic engine process and the aftertreatment system, together with their interaction in a single simulation environment. Regarding the aftertreatment system, the kinetic reaction behavior of state-of-the-art and advanced components, such as Diesel Oxidation Catalysts (DOC) or Selective Catalytic Reduction Soot Filters (SCRF), is being modelled. Furthermore, the authors present the use of the 1D engine and exhaust gas aftertreatment model on use cases of variable valve train (VVT) applications on passenger car (PC) diesel engines.

In order to reduce engine out CO2 emissions it is a main subject to find new alternative fuels out of renewable sources. For this paper, several fuels were selected which can be produced out of biomass or with hydrogen which is generated directly via electrolysis with electricity from renewable sources. All fuels are compared to conventional diesel fuel and two diesel surrogates. It is well known that there can be a large effect of fuel properties on mixture formation and combustion, which may result in a completely different engine performance compared to the operation with conventional diesel fuels. Mixture formation and ignition behavior can also largely affect the pollutant formation. The knowledge of the combustion behavior is also important to design new engine geometries or implement new calibrations for an existing engine. The fuel properties of the investigated fuels comprise a large range, for example in case of the derived cetane number, from below 30 up to 100.

Within the Cluster of Excellence “Tailor-Made Fuels from Biomass” (TMFB) at the RWTH Aachen University, two novel biogenic fuels, namely 1-octanol and its isomer dibutyl ether (DBE), were identified and extensively analyzed in respect of their suitability for combustion in a Diesel engine. Both biofuels feature very different properties, especially regarding their ignitability. In previous works of the research cluster, promising synthesis routes with excellent yields for both fuels were found, using lignocellulosic biomass as source material. Both fuels were investigated as pure components in optical and thermodynamic single cylinder engines (SCE). For 1-octanol at lower part load, almost no soot emission could be measured, while with DBE the soot emissions were only about a quarter of that with conventional Diesel fuel. At high part load (2400 min-1, 14.8 bar IMEP), the soot reduction of 1-octanol was more than 50% and for DBE more than 80 % respectively.

The influence of two oxygenated tailor-made fuels on soot formation and oxidation in an optical single cylinder research diesel engine has been studied. For the investigation a planar laser-induced incandescence (PLII) measurement technique was applied to the engine in order to detect and evaluate the planar soot distribution for the two bio fuels within a laser light sheet. Furthermore the OH* chemiluminescence and broad band soot luminosity was visualized by high speed imaging to compare the ignition and combustion behavior of tested fuels: Two C8 oxygenates, di-n-butylether (DNBE) and 1-octanol. Both fuels have the same molecular formula but differ in their molecular structure. DNBE ignites fast and burns mostly diffusive while 1-octanol has a low cetane number and therefore it has a longer ignition delay but a more homogeneous mixture at time of ignition. The two bio fuels were finally compared to conventional diesel fuel.

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.

Model-based control strategies along with an adapted calibration process become more important in the overall vehicle development process. The main drivers for this development trend are increasing numbers of vehicle variants and more complex engine hardware, which is required to fulfill the more and more stringent emission legislation and fuel consumption norms. Upcoming fundamental changes in the homologation process with EU 6c, covering an extended range of different operational and ambient conditions, are suspected to intensify this trend. One main reason for the increased calibration effort is the use of various complex aftertreatment technologies amongst different vehicle applications, requiring numerous combustion modes. The different combustion modes range from heating strategies for active Diesel Particulate Filter (DPF) regeneration or early SCR light-off and rich combustion modes to purge the NOx storage catalyst (NSC) up to partially premixed combustion modes.