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The software design of this new engine control unit is based on a unique and homogenous torque structure. All input signals are converted into torque equivalents and a torque coordinator determines their influence on the final torque delivered to the powertrain. The basic torque structure is independent on the type of fuel and can be used for gasoline, diesel, or CNG injection systems. This allows better use of custom specific algorithms and facilitates reusability, which is supported by the graphical design tool that creates all modules using automatic code generation. Injection specific algorithms can be linked to the software by simply setting a software switch.

In power-split configuration, the input power is split into two parts, one of which is transmitted from the internal combustion engine through one or more planetary gear(s) to the wheels. The other part is generated as electricity and passes through an electrical variator to assist the driving torque. The latter has the characteristic of poor efficiency. In this simulation study, a comparison among the input power-split, compound power-split, and two mode power-split are discussed. Output power-split is not mentioned in this paper due to its limited applicability in specific vehicles. The idea of selection of the electrical machines is explained: the speed and torque of electrical machines was taken into consideration for the required transmission ratios spread.

Battery simulation by a DSP-controlled high current power supply is used to improve repeatability and comparability of starting tests, especially at low temperatures. The simulator's algorithm calculates the internal resistance of the battery by a timely constant resistor and a variable resistor representing the actual discharge history. The output voltage of the simulator is set as a function of internal resistor and load current with temperature and state of charge as setup parameter. The simulator was evaluated in cold start testing in comparison to real batteries. As a result, batteries are simulated with high repeatability. Deviations to real battery behavior are in the range of test to test deviations using real batteries.

The current legislation for industrial applications and construction equipment including earthmoving machines and crane engines allows different strategies to fulfill the corresponding exhaust emission limits. Liebherr Machines Bulle SA developed their engines to accomplish these limits using SCRonly technology. IAV supported this development, carrying out engine as well as SCR aftertreatment system and vehicle calibration work including the OBD and NOx Control System (NCS) calibration, as well as executing the homologation procedures at the IAV development center. The engines are used in various Liebherr applications certified for EU Stage IIIb, EPA TIER 4i, China GB4 and IMO MARPOL Tier II according to the regulations “97/68/EC”, “40 CFR Part 1039”, “GB17691-2005” and “40 CFR Parts 9, 85, et al.” using the same SCR hardware for all engine power variants of the corresponding I6 and V8 engine families.

A physics-based simulation program developed by IAV is used to calibrate the torque structure and cylinder charge calculation in the electronic control unit of SI engines. The model calculates both the charge cycle and combustion phase based on flow mechanics and a fractal combustion model. Once the air mass in the charge cycle has been computed, a fractal combustion model is used for the ongoing calculation of cylinder pressure and temperature. The progression of cylinder pressure over the high and low-pressure phases also provides information on engine torque. Following the engine-specific calibration of the model using elemental geometric information and reduced test bench measurements, the physical engine properties can be simulated over the operating cycle. The calibrated model allows simulations to be carried out at all operating points and the results to be treated as virtual test bench measurements.

Recent years have witnessed a dramatic increase in global ethanol production, while cellulosic feedstock or the algae-based production approach make more sustainable ethanol production foreseeable in many countries. The ethanol produced will increasingly penetrate the markets not only as blending component, but also as main fuel component, boosting demand for flex-fuel vehicles. One of the main challenges for flex-fuel vehicles is the cold start due to the poor vapor pressure of ethanol. This is detrimental to starting capability in DISI engines in particular, with increased cylinder wall wetting causing higher oil dilution. The most efficient solution for DISI engines is a smart injection strategy, enabling fuel vaporization during injection in the compression stroke. But this requires optimum injection parameters such as injection timing, split ratio and rail pressure.

The development of energy efficient air conditioning systems for electric vehicles is an ever increasing challenge, because the cooling as well as the heating of the passenger compartment reduces the cruising range dramatically. Electric cars are usually equipped with a scroll compressor and a separate electric motor with appropriate power electronics. However, this solution is critical in terms of the installation space, the weight and also the costs. Therefore, an innovative and energy efficient drivetrain structure for electric vehicles was developed, which integrates the motor of the A/C-compressor directly into the drivetrain. Thus it is possible to switch off the compressor motor and to use the main motor for the drive of the compressor at certain driving situations. As a result, the operating point of the main motor can be shifted to a better efficiency.

The powertrain electrification is currently not only taking place in public road mobility vehicles, but is also making its way to the racetrack, where it’s driving innovation for developments that will later be used in series production vehicles. The current development focus for electric vehicles is the balance between driving power, range and weight, which is given even greater weighting in racing. To redefine the current limits, IAV developed a complete e-powertrain for a racing MX motorcycle and integrated it into a real drivable demonstrator bike. The unique selling point is the innovative direct phase-change cooling (PCC) of the three-phase e-motor and its power electronics, which enables significantly increased continuous power (Pe = 40 kW from 7,000 rpm to 9,000 rpm) without thermal power reduction. The drive unit is powered by a replaceable Lithium-Ion round cell battery (Ubat,max = 370V) with an energy storage capacity of Ebat = 5 kWh.

This paper proposes a current limits distribution control strategy for a parallel hybrid electric vehicle (parallel HEV) which includes an advanced powertrain concept with two electrical driving axles. One of the difficulties of an HEV powertrain with two electrical driving axles is the ability to distribute the electrical current of one high voltage battery appropriately to the two independent electrical motors. Depending on the vehicle driving condition (i.e., car maneuver) or the maximization of the entire efficiency chain of the system, a suitable control strategy is necessary. We propose an input-output feedback linearization strategy to cope with the nonlinear system subject to input constraints. This approach needs an external, state dependent saturation element, which translates the state dependent control input saturation to the new feedback linearizing input and therefore preserves the properties of the differential geometric framework.

This paper proposes a current limits distribution control strategy for a parallel hybrid electric vehicle (parallel HEV) which includes an advanced powertrain concept with two electrical driving axles. One of the difficulties of an HEV powertrain with two electrical driving axles is the ability to distribute the electrical current of one high voltage battery appropriately to the two independent electrical motors. Depending on the vehicle driving condition (i.e., car maneuver) or the maximization of the entire efficiency chain of the system, a suitable control strategy is necessary. We propose an input-output feedback linearization strategy to cope with the nonlinear system subject to input constraints. This approach needs an external, state dependent saturation element, which translates the state dependent control input saturation to the new feedback linearizing input and therefore preserves the properties of the differential geometric framework.

The future emission standards, including real driving emissions (RDE) measurements are big challenges for engine and after-treatment development. Also for development of a robust control system, in real driving emissions cycles under varied operating conditions and climate conditions, like low ambient temperature as well as high altitude are advanced physical-based algorithms beneficial in order to realize more precise, robust and efficient control concepts. A fast-running novel physical-based ignition delay model for diesel engine combustion simulation and additionally, for combustion control in the next generation of ECUs is presented and validated in this study. Detailed chemical reactions of the ignition processes are solved by a n-heptane mechanism which is coupled to the thermodynamic simulation of in-cylinder processes during the compression and autoignition phases.

To meet the targets of Indian future emission legislation, an electrification and automation of today’s manual transmission technology is necessary. For this reason, IAV invented an electrified automated transmission family, based on well-known manual transmission technology. This low-cost automated manual transmission (AMT) approach is equipped with a 48 V electric machine and can be used as pure electric or hybrid drivetrain. Furthermore, it is possible to realize power shifts by using just one dry friction element. A small number of standard components combined with a low voltage electric machine and an electromechanical actuation system is sufficient to create a maximum of flexibility to meet future emission fleet targets, without having the disadvantageous high costs for a high-voltage electric system. To detect the optimal powertrain configuration, IAV used a unique advance development tool called Powertrain Synthesis.

Whereas the verification of non-safety-related, embedded software typically focuses on demonstrating that the implementation fulfills its functional requirements, this is not sufficient for safety-relevant systems. In this case, the control software must also meet application-specific safety requirements. Safety requirements typically arise from the application of hazard and/or safety analysis techniques, e.g. FMEA, FTA or SHARD. During the downstream development process it must be shown that these requirements cannot be violated. This can be achieved utilizing different techniques. One way of providing evidence that violations of the safety properties identified cannot occur is to thoroughly test each of the safety requirements. This paper introduces Evolutionary Safety Testing (EST), a fully automated procedure for the safety testing of embedded control software.

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.

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.

Flow fields and fuel distribution play a critical role in developing the combustion process inside the cylinders of piston engines. This has prompted the development of measurement and diagnostic capabilities including laser techniques like Doppler Global Velocimetry (DGV). The paper provides an overview of the basics of DGV and the type of results that can be obtained. It also includes a short comparison to Particle Image Velocimetry (PIV) which is a popular alternative method. Furthermore, it is shown that DGV can be used simultaneously in combination with droplet distribution visualization inside cylinders based on Mie scattering.

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.

Conventional methods of physicochemical models require various experts and a high measurement demand to achieve the required model accuracy. With an additional request for faster development time for diagnostic algorithms, this method has reached the limits of economic feasibility. Machine learning algorithms are getting more popular in order to achieve a high model accuracy with an appropriate economical effort and allow to describe complex problems using statistical methods. An important point is the independence from other modelled variables and the exclusive use of sensor data and actuator settings. The concept has already been successfully proven in the field of modelling for exhaust gas aftertreatment sensors. An engine-out nitrogen oxide (NOX) emission sensor model based on polynomial regression was developed, trained, and transferred onto a conventional automotive electronic control unit (ECU) and also proves real-time capability.

After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions.

The majority of powertrain types considered important contributors to achieving the CO2 targets in the transportation sector employ a battery as an energy storage device. The need for batteries is hence expected to grow drastically with increasing market share of CO2-optimized powertrain concepts. The resulting huge pressure on the development of future electrochemical energy storage systems necessitates the application of advanced methodologies enabling a fast and cost-efficient concept definition and optimization process. This paper presents a model-based methodology for the optimization of BEV thermal management concept layouts and operation strategies targeting minimized energy consumption. Starting at the vehicle level, the proposed methodology combines appropriate representations of all primary powertrain components with 1D cooling and refrigerant circuit models and focuses on their interaction with the battery chemistry.