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In steer-by-wire (SbW) vehicles, understanding the steering rack force is essential to replicate a realistic steering feel, allowing conclusions to be drawn about road surface conditions by the decoupled manual actuator. Since internal friction varies with each steering system manufactured and installed, these models differ greatly in accuracy. This paper presents a concept for continuously calculating fluctuating friction based on the internal steering variables to avoid additional and complex individual measurements. An SbW system offers the right approach by adjusting the driver’s desired steering angle and the required motor control. The underlying steering clearance and the Kalman filter are used to calculate the steering rack force. The validity of the proposed concept is shown in drive tests according to ISO 13674 and ISO 7401 to gauge high and low friction values in different speed ranges.

The accelerated processes in vehicle development require new technologies for function development and validation. With this motivation, Function-in-the-Loop (FiL) simulation was developed as a link between Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) simulation. The combination of real Electronic Control Unit (ECU) hardware and software in conjunction with virtual components is very well suited for function development and testing. This approach opens up new possibilities for mechatronic systems that would otherwise require special test benches. For this reason, an Electric Power Steering (EPS) was transferred to a virtual environment using FiL simulation. This enables a wide range of applications, from EPS testing to the development of connected driving functions on an integrated platform. Right from the early development phases, the technology can be used purposefully with short integration cycles.

Meeting strict current and future emissions legislation necessitates development of computational tools capable of predicting the behaviour of combustion and emissions with an accuracy sufficient to make correct design decisions while keeping computational cost of the simulations amenable for large-scale design space exploration. While detailed kinetics modelling is increasingly seen as a necessity for accurate simulations, the computational cost can be often prohibitive, prompting interest in simplified approaches allowing fast simulation of reduced mechanisms at coarse grid resolutions appropriate for internal combustion engine simulations in design context. In this study we present a simplified Well-stirred Reactor (WSR) implementation coupled with 3D CFD Ricardo VECTIS solver.

Future heavy-duty (HD) concepts should fulfill very tight tail-pipe NOx emissions and simultaneously fulfill the fuel efficiency targets. In current HD Euro VII discussions, real working cycles become key to ensure emission conformity. For instance, cold start and cold ambient conditions during testing with low load profiles starting from 0% payload, require external heating measures. Knowing the trade-off between fuel consumption and tail-pipe NOx emissions a holistic engine and EAT system optimization with innovative thermal management is required. Towards a carbon neutral mobility, Hydrogen combustion engines are one of the key solutions. Advanced combustion system development enables maximal usage of lean burning as the major advantage of the Hydrogen fuel for efficiency improvement and NOx reduction.

For the NOx removal from diesel exhaust, the selective catalytic reduction (SCR) and lean NOx traps are established technologies. However, these procedures lack efficiency below 200 °C, which is of importance for city driving and cold start phases. Thus, the present paper deals with the development of a novel low-temperature deNOx strategy implying the catalytic NOx reduction by hydrogen. For the investigations, a highly active H2-deNOx catalyst, originally engineered for lean H2 combustion engines, was employed. This Pt-based catalyst reached peak NOx conversion of 95 % in synthetic diesel exhaust with N2 selectivities up to 80 %. Additionally, driving cycle tests on a diesel engine test bench were also performed to evaluate the H2-deNOx performance under practical conditions. For this purpose, a diesel oxidation catalyst, a diesel particulate filter and a H2 injection nozzle with mixing unit were placed upstream to the full size H2-deNOx catalyst.

Hydrogen engines offer the possibility of a carbon neutral transportation - a focal point of current propulsion development activities especially for EU and US future concepts. From today's point of view, hydrogen can play an important role in this regard as it is a carbon-free fuel, no CO2 emissions are produced during its combustion process. Besides, it can be well used for lean burn combustion leading to very low NOx emissions, a key benefit in combination with an optimized after-treatment system for future ultra-low NOx legislations of heavy-duty (HD) engines. Comprehensive investigations using experimental tests and model-based development approach are performed using a six cylinder HD hydrogen engine featuring PFI (port fuel injection) aiming the definition of a high efficiency hydrogen engine concept.

High pressure EGR provides NOx emission reduction even at low exhaust temperatures. To maintain a safe EGR system operation over a required lifetime, the EGR cooler fouling must not exceed an allowable level, even if the engine is operated under worst-case conditions. A reliable fouling simulation model represents a valuable tool in the engine development process, which validates operating and calibration strategies regarding fouling tendency, helping to avoid fouling issues in a late development phase close to series production. Long-chained hydrocarbons in the exhaust gas essentially impact the fouling layer formation. Therefore, a simulation model requires reliable input data especially regarding mass flow of long-chained hydrocarbons transported into the cooler. There is a huge number of different hydrocarbon species in the exhaust gas, but their individual concentration typically is very low, close to the detection limit of standard in-situ measurement equipment like GC-MS.

The newest legislative trends enforce a significant decrease in CO2 emissions for commercial vehicles. For instance, in Europe a drop in fleet consumption of 15% and 30% is set as target by the regulation by 2025 and 2030. The use of carbon-neutral fuels offers possibilities regarding net-zero CO2 emissions - although not yet considered by the rules. Another challenging aspect is the drastic tightening of NOx emissions limits for future legislations, which is approved or being discussed both for the United States and for the EU. The current work describes the potentials of an innovative fuel, marketed as Gane fuel regarding performance, efficiency and emission behavior. First, the properties of the developed fuel are described: Gane is made from methanol blended with water and is tailored for diffusive combustion. The fuel blending is so defined to fulfill the combustion requirements.

One challenge for the development of commercial vehicles is the reduction of CO2 greenhouse, where hydrogen can help to reduce the fleet CO2. For instance, in Europe a drop in fleet consumption of 15% and 30% is set as target by the regulation until 2025 and 2030. Another challenge is EURO VII in EU or even already approved CARB HD Low NOx Regulation in USA, not only for Diesel but also for hydrogen combustion engines. In this study, first the requirements for the combustion and after-treatment system of a hydrogen engine are defined based on future emission regulations. The major advantages regarded to hydrogen combustion are due to the wide range of flammability and very high flame speed numbers compared to other fossil based fuels. Thus, it can be well used for lean burn combustion with much better fuel efficiency and very low NOx emissions with an ultra lean combustion. A comprehensive experimental investigation is performed on a HD 2 L single-cylinder engine.

As the late combustion phase in SI engines is of high importance for a further reduction of fuel consumption and especially emissions, the impacts of unburnt mass, located in a small volume with a relatively large surface near the wall and in the top land volume, is of high relevance throughout the range of operation. To investigate and quantify the respective interactions, a state of the art Mercedes-Benz single cylinder research SI-engine was equipped with extensive measurement technology. To detect the axial and radial temperature distribution, several surface thermocouples were applied in two layers around the top land volume. As an additional reference, multiple surface thermocouples in the cylinder head complement the highly dynamic temperature measurements in the boundary zones of the combustion chamber.

The development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine.

Reduction of the CO2 greenhouse gas emissions is one major challenge the automotive industry as a part of the transportation sector is facing. Hydrogen is regarded as one of the key energy solutions for CO2 reduction in the future transportation sector. First, a hydrogen-powered single-cylinder test rig for 2 liter heavy-duty engine will be introduced. Followed by a discussion of experimental results including variations of engine speed, torque, ignition strategy, air-fuel ratio, etc. In addition, the paper proposes a new phenomenological model for the prediction of hydrogen combustion. The model is based on the well-known two-zone Entrainment approach, supported by newly developed hydrogen-specific submodels for the calculation of the laminar flame speed and auto-ignition in the unburned mass zone. The developed physical-based combustion model is extensively validated based on the experimental single-cylinder results.

To date, no generally valid statements can be made about the service life of brake pads, which may be due to factors such as driving style, the friction material used or the varying vehicle weight. While dynamic friction models including friction history are already established [1], the investigation of wear and wear dust behavior is currently in the focus of many research projects. One example is the investigation of calculation models for brake pad wear while neglecting the temperature development in the brake [2]. In cars, temperatures of up to 800°C occur in the brake under high loads, which leads to a significant increase in wear. Accordingly, the question arises how an estimation of brake pad wear can be applied to highly dynamic load cases. To do this, however, the processes taking place in the boundary layer between pad and disc must first be comprehensively understood and described.

Two substantially different methods have become popular in building fast computing catalyst models: physico-chemical approaches focusing on dimensionality reduction and machine learning approaches. Data driven models are known to be very fast computing and to achieve high accuracy but they can lack of extrapolation capability. Physico-chemical models are usually slower and less accurate but superior regarding robustness. The robustness can even be reinforced by implementing an extended Kalman filter, which enables the model to adapt its states based on actual sensor values, even if the sensors are drifting. The present study proposes a combination of both approaches into one hybrid model, keeping the robustness of the physico-chemical model in edge cases while also achieving the accuracy of the data based model in well-known regimes. The output of the hybrid model is controlled by an autoencoder, utilizing methods well known from the field of anomaly detection.

The latest legislative tendencies for on-highway heavy duty vehicles in the United States such as the feasibility assessment of low NOX standards of CARB or EPA’s memorandum forecast further tightening of the NOX emissions limits. In addition, the GHG Phase 2 legislation and also phased-in regulations in the EU enforce a continuous reduction in CO2 emissions resp. fuel consumption. In order to meet such low NOX emission limits, a rapid heat-up of the exhaust after-treatment system (EATS) is inevitable. However, the required thermal management results in increased fuel consumption, i.e. CO2 emissions as shown in numerous previous works also by the authors. A NOX-CO2 trade-off for cumulative cycle emissions can be observed, which can be optimized by using more advance technologies on the engine and/or on the EATS side.

A practical Gasoline Compression Ignition (GCI) concept is presented that works on standard European 95 RON E10 gasoline over the whole speed/load range. A spark is employed to assist the gasoline autoignition at low loads; this avoids the requirement of a complex cam profile to control the local mixture temperature for reliable autoignition. The combustion phasing is controlled by the injection pattern and timing, and a sufficient degree of stratification is needed to control the maximum rate of pressure rise and prevent knock. With active control of the swirl level, the combustion system is found to be relatively robust against variability in charge motion, and subtle differences in fuel reactivity. Results show that the new concept can achieve very low fuel consumption over a significant portion of the speed/load map, equivalent to diesel efficiency. The efficiency is worse than an equivalent diesel engine only at low load where the combustion assistance operates.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

In order to reduce the negative health effects associated with engine pollutants, environmental problems caused by combustion engine emissions and satisfy the current strict emission standards, it is essential to better understand and simulate the emission formation process. Further development of emission model, improves the accuracy of the model-based optimization approach, which is used as a decisive tool for combustion system development and engine-out emission reduction. The numerical approaches for emission simulation are closely coupled to the combustion model. Using a detailed emission model, considering the 3D mixture preparation simulation including, chemical reactions, demands high computational effort. Phenomenological combustion models, used in 1D approaches for model-based system optimization can deliver heat release rate, while using a two-zone approach can estimate the NOx emissions.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.

The reduction of both harmful emissions (CO, HC, NOx, etc.) and gases responsible for greenhouse effects (especially CO2) are mandatory aspects to be considered in the development process of any kind of propulsion concept. Focusing on ICEs, the main development topics are today not only the reduction of harmful emissions, increase of thermodynamic efficiency, etc. but also the decarbonization of fuels which offers the highest potential for the reduction of CO2 emissions. Accordingly, the development of future ICEs will be closely linked to the development of CO2 neutral fuels (e.g. biofuels and e-fuels) as they will be part of a common development process. This implies an increase in development complexity, which needs the support of engine simulations. In this work, the virtual modeling of real fuel behavior is addressed to improve current simulation capabilities in studying how a specific composition can affect the engine performance.