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In this paper different compressor/expander concepts including mechanical superchargers, turbochargers and two-stage charging concepts are analysed with regard to their suitability for fuel cell applications. Special attention is focused on system designs which use the energy of the tail gases for driving the compressor. The net efficiencies of different system concepts at full load were calculated with a simulation model, based on Matlab/Simulink‘ and show, that with a single stage turbocharger in combination with a tail gas burner good efficiencies and high power densities can be obtained at a pressure level of more than 2.5 bar.

In this paper, boosting strategies are investigated for part load operation of typical fuel-cell-systems. The optimal strategy can mainly be obtained by simulation. The boosting strategy is one of the most essential parameters for design and operation of a fuel-cell-system. High pressure ratios enable high power densities, low size and weight. Simultaneously, the demands in humidification and water recovery for today's systems are reduced. But power consumption and design effort of the system increases strongly with the pressure level. Therefore, the main focus must be on the system efficiencies at part load. In addition, certain boundary conditions like the inlet temperature of the fuel-cell stack must be maintained. With high pressure levels the humidification of the intake air before, within or after the compressor is not sufficient to dissipate enough heat. Vaporization during the compression process shows efficiency advantages while the needs in heat dissipation decreases.

In this article, the development challenges of a fuel cell system are explained using the example of the BREEZE! fuel cell range extender (FC-REX) applied in an FEV Liiona. The FEV Liiona is a battery electric vehicle based on a Fiat 500 developed by FEV. The BREEZE! system is the first applied 30 kW low temperature polymer electrolyte membrane (LT PEM) fuel cell system in the subcompact vehicle class. Due to the highly integrated system approach and dry cathode operation, a compact design of the range extender module with a system power density of 0.45 kW/l can be achieved so that the vehicle interior including trunk remains completely usable. System development for fuel cells significantly influences performance, efficiency, package, durability, and required maintenance effort of a fuel cell electric powertrain. In order to ensure safe and reliable operation, the fuel cell system has to be supplied with sufficient amounts of air, hydrogen, and coolant flows.

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.

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.

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.

In this paper different air management system concepts including mechanical superchargers and turbochargers are analysed with regard to their suitability for fuel cell applications. Therefore a simulation model which takes the main mass, energy and heat flows in the fuel cell system including fuel evaporation, reformer, gas cleaning, humidification, burner and compressor/expander unit into account was setup. For a PEM system with methanol steam reformer the best system efficiencies at rated power can be achieved with a turbocharger in combination with a tailgas burner for operating pressures between 2.5 and 2.8 bar. For pure hydrogen systems the best system efficiency is obtained with an electric driven supercharger for a maximum pressure of 2 bar and an appropriate pressure strategy during part load operation in the complete operating range. The increase of system efficiency for pressurized stack operation is mainly attributed to advantages with regard to water management.

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 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.

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.

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.

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.

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.

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.