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Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

In spark-ignition engines, fluctuations of the in-cylinder pressure trace and the apparent rate of heat release are usually observed from one cycle to another. These Cycle-to-Cycle Variations (CCV) are affected by the early flame development and the subsequent flame front propagation. The CCV are responsible for engine performance (e.g. fuel consumption) and the knock behavior. The occurrence of the phenomena is unpredictable and the stochastic nature offers challenges in the optimization of engine control strategies. In the present work, CCV are analyzed in terms of their impact on the engine knock behavior and the related efficiency. Target is to estimate the possible fuel consumption savings in steady-state operation and in the drivecycle, when CCV are reduced. Since CCV are immanent on real engines, such a study can only be done by means of simulation.

In the present work, a scalable simulation methodology is presented that enables the assessment of the impact of SI-engine cycle-to-cycle combustion variations on fuel consumption and hence CO2 emissions on three different levels of modeling depth: in-cylinder, steady-state engine and transient engine and vehicle simulation. On the detailed engine combustion chamber level, a 3D-CFD approach is used to study the impact of the turbulent in-cylinder flow on the cycle-resolved flame propagation characteristics. On engine level, cycle-to-cycle combustion variations are assessed regarding their impact on indicated mean effective pressure, aiming at estimating the possible fuel consumption savings when cyclic variations are minimized. Finally, on the vehicle system level, a combined real-time engine approach with crank-angle resolved cylinder is used to assess the potential fuel consumption savings for different vehicle drivecycle conditions.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Global vehicle simulation is already a well-established tool to support the vehicle development process. In contrast to conventional vehicles, electrified powertrains offer an additional challenge to the thermal conditioning: the durability of E-components is not only influenced by temperature peaks but also by the duration and amplitude of temperature swings as well as temperature gradients within the components during their lifetime. Keeping all components always at the preferred lowest temperature level to avoid ageing under any conditions (driving, parking, etc.) will result in very high energy consumption which is in contradiction to the efficiency targets.

In order to improve the accuracy of vehicle simulation under transient cycle conditions and thus predict performance and fuel consumption, consideration of the complete system engine/drivetrain/vehicle is necessary. The coupling of otherwise independent simulation programs is therefore necessary for the vehicle and engine. The description of thermally transient processes enables the calculation of the heat balance of the engine, which in turn enables the simulation of warming up operation. Through consideration of the engine warming up process, the quality of the prediction of fuel consumption and emissions is improved. The combination of the simulation programs CRUISE and BOOST to determine the engine heat balance has proven to be successful for the analysis of transient drive cycles.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

Increasing powertrain complexity and the growing number of vehicle variants are putting a strain on current calibration development processes. This is particularly challenging for vehicle drivability calibration, which is traditionally completed late in the development cycle, only after mature vehicle hardware is available. Model-based calibration enables a shift in development tasks from the real world to the virtual world, allowing for increased system robustness while reducing development costs and time. A unique approach for drivability calibration was developed by incorporating drivability analysis software with online optimization software into a virtual engine test cell environment. Real-time, physics-based engine and vehicle simulation models were coupled with real engine controller hardware and software to execute automated drivability calibration within this environment.

Approaching engineering limits for the thermal design of battery modules requires virtual prototyping and appropriate models with respect to physical depth and computational effort. A multi-scale and multi-domain model describes the electrochemical behavior of a single battery unit cell in 1D+1D at the level of intra-cell phenomena, and it applies a 3D thermal model at module level. Both models are connected within a common vehicle simulation platform. The models are discussed with special emphasis on battery degradation such as solid electrolyte interphase layer formation, decomposition and lithium plating. The performance of the electrochemical model is assessed by discharge cycles and repeated charge/discharge simulations. The thermal module model is compared to CFD reference data and studied with respect to its grid sensitivity.

The world of automotive engineering shows a clear direction for upcoming development trends. Stringent fleet average fuel consumption targets and CO2 penalties as well as rising fuel prices and the consumer demand to lower operating costs increases the engineering efforts to optimize fuel economy. Passenger car engines have the benefit of higher degree of technology which can be utilized to reach the challenging targets. Variable valve timing, downsizing and turbo charging, direct gasoline injection, highly sophisticated operating strategies and even more electrification are already common technologies in the automotive industry but can not be directly carried over into a motorcycle application. The major differences like very small packaging space, higher rated speeds, higher power density in combination with lower production numbers and product costs do not allow implementation such high of degree of advanced technology into small-engine applications.

The well-to-wheel CO2 emissions and energy use of internal combustion engines (diesel and gasoline) are compared to fuel cell automotive propulsion systems. The fuel cell technologies investigated are polymer electrolyte fuel cell (PEFC), alkaline fuel cell (AFC) and solid oxide fuel cell (SOFC). The fuels are assumed to be produced from either crude oil or natural gas. The comparison is based on driving cycle simulations of a mid-class passenger car with an inertia test weight of 1350 kg. The study shows that the optimized diesel drive train (downsized mated to an integrated starter generator) achieves the best overall energy efficiency. The lowest CO2 emissions are produced by compressed natural gas (CNG) vehicles. Fuel cell propulsion systems achieve similar or even better CO2 emission values under hot start conditions but suffer from high energy input required during warm-up.

The present work introduces an innovative mechanistically based 0D spray model which is coupled to a combustion model on the basis of an advanced mixture controlled combustion approach. The model calculates the rate of heat release based on the injection rate profile and the in-cylinder state. The air/fuel distribution in the spray is predicted based on momentum conservation by applying first principles. On the basis of the 2-zone cylinder framework, NOx emissions are calculated by the Zeldovich mechanism. The combustion and emission models are calibrated and validated with a series of dedicated test bed data specifically revealing its capability of describing the impact of variations of EGR, injection timing, and injection pressure. A model based optimization is carried out, aiming at an optimum trade-off between fuel consumption and engine-out emissions. The findings serve to estimate an economic optimum point in the NOx/BSFC trade-off.

The present work introduces a fully integrated real-time (RT) capable engine and vehicle model. The gas path and drive line are described in the time domain of seconds whereas the reciprocating characteristics of an IC engine are reflected by a crank angle resolved cylinder model. The RT engine model is derived from a high fidelity 1D cycle simulation and gas exchange model to support an efficient and consistent transfer of model data like geometries, heat transfer or combustion. The workflow of model calibration and application is outlined and base ECU functionalities for boost pressure, EGR, smoke and idle speed control are applied for transient engine operation. Steady state results of the RT engine model are compared to experimental data and 1D high fidelity simulations for 19 different engine load points. In addition an NEDC (New European Drive Cycle) is simulated and results are evaluated with data from chassis dynamometer measurements.

Today's powertrains are becoming more and more complex due to the increasing number of gear box types requiring gearshift patterns like conventional (equipped with GSI) and automatic-manual transmissions (AT, AMT), double clutch and continuous variable transmissions (DCT, CVT). This increasing variety of gear boxes requires a higher effort for the overall optimization of the powertrain. At the same time, it is necessary to assess the impact of different powertrains and control strategies on CO₂ emissions very early in the development process. The optimization of Gear Shift Patterns (G.S.P.) has to fulfill multiple constraints in terms of objective customers' requirements, like driveability, NVH, performance, emissions and fuel consumption. For these reasons, RENAULT and AVL entered an engineering collaboration in order to develop a dedicated simulation tool: CRUISE GSP.

Fuel consumption is the most important contributor to the total cost of ownership for mass produced motorcycles. Therefore, best fuel economy is one main influencing criteria for a decision to purchase motorcycles. Furthermore, increasingly stringent emission legislations limit and additional OBD requirements must be fulfilled. A new combined test approach has been developed that minimizes accuracy losses in the development process which compensates for the variability of driving behavior in the chassis dyno environment. An engine testbed combined with a belt drive transmission enables operation in single engine or in Powerpack (i.e. internal combustion engine including transmission) configuration as well as under steady state or dynamic operating mode. Since the belt drive transmission is integrated in the test rig, realistic inertia situation for the single engine operating test configuration is ensured.

Best fuel efficiency is one of the core requirements for commercial vehicles in India. Consequently it is a central challenge for commercial vehicle OEMs to optimize the entire powertrain, hence match engine, transmission and rear axle specifications best to the defined application. The very specific real world driving conditions in India (e.g. traffic situations, road conditions, driver behavior, etc.) and the large number of possible commercial powertrain combinations request an efficient and effective development methodology. This paper presents a methodology and tool chain to specify and develop commercial powertrains in a most efficient and effective way. The methodology is based on the measurement of real world driving scenarios, identification of representative Real World Driving Profiles and vehicle system simulation which allows extended analysis of the road topography, the traffic situation as well as the driver behavior.

The incorporation of a detailed engine process calculation that takes into account thermal behavior of the engine and exhaust system is essential for a realistic simulation of transient vehicle operation. This is the only possible way to have a precise preliminary calculation of fuel consumption and emissions. Therefore, a comprehensive thermal network of the engine based on the lumped capacity method has been developed. The model allows the computation of component temperatures in steady state operation as well as in transient engine studies, e.g. investigations of engine warm-up. The model is integrated in a co-simulation environment consisting of a detailed vehicle and engine cycle simulation code. The paper describes the procedure of the co-simulation and presents several examples of warm-up simulations.

This work presents a physical model that calculates the efficiency maps of the inverter-fed Permanent Magnet Synchronous Machine (PMSM) drive. The corresponding electrical machine and its controller are implemented based on the two-phase (d-q) equivalent circuits that take into account the copper loss as well as the iron loss of the PMSM. A control strategy that optimizes the machine efficiency is applied in the controller to maximize the possible output torque. In addition, the model applies an analytical method to predict the losses of the voltage source inverter. Consequently, the efficiency maps within the entire operating region of the PMSM drive can be derived from the simulation results, and they are used to represent electric drives in the system simulation model of electric vehicles (EVs).

This paper presents an overview of the results on heavy duty engines collected in the “PARTICULATES” project, which aimed at the characterization of exhaust particle emissions from road vehicles. The same exhaust gas sampling and measurement system as employed for the measurements on light duty vehicles [1] was used. Measurements were made in three labs to evaluate a wide range of particulate properties with a range of heavy duty engines and fuels. The measured properties included particle number, with focus separately on nucleation mode and solid particles, particle active surface and total mass. The sample consisted of 10 engines, ranging from Euro-I to prototype Euro-V technologies. The same core diesel fuels were used as in the light duty programme, mainly differentiated with respect to their sulphur content. Additional fuels were tested by some partners to extend the knowledge base.

For the development of complex control algorithms and strategies the engine and powertrain test bed offers a number of advantages over the development in the prototype vehicle. The paper discusses how state-of-the-art simulation techniques can contribute to a continuous development process, which is based upon offline simulation using hardware in the loop, the utilization of modern test bed technology up to vehicle adjustment. The integration of hardware-in-the-loop testing together with vehicle and transmission simulation on the testbed allows to speed up the optimization of fuel consumption, emissions and driveability in an early stage in the development process. The available software tools are presented and application examples are given.

The correct information about legal demands of the On-Board-Diagnostic (OBD) system in a vehicle project is required throughout the entire development process. Usually, the main obstacle in succeeding is to provide the company's expertise of some few experts for all employees who work in OBD related projects. The paper describes the AVL solution for knowledge management and tool supported control system design and calibration: OBD System Development Database. The software enables the user to access the regulatory requirements for a specific application and legislation from past, present and future (proposed rule-making) point of view. Information concerning already available and stored monitoring concepts is linked to the requirements in order to re-use potentially suitable concepts and to enable an efficient knowledge exchange within the company.