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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.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

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.

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).

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.

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.

Despite the increasingly stringent emissions legislation, users and owners of commercial diesel vehicles are continually demanding that each new engine generation is more economical than the previous one. This is especially important for commercial vehicles where the majority of engines are in the 1-2ltr./cyl. class. The demands are being reflected in new engine designs with lower friction and improved structural stiffness, together with fuel systems having increased pressure capability, higher spill rates, injection rate shaping and advanced control features. These fuel system requirements have led to a variety of new fuel injection systems and in the search for increased injection pressure these fuel systems have placed greater demands on the engine, especially in areas such as the cylinder head and fuel system drive, sometimes with adverse effects on the combustion and fuel injection system induced mechanical noise.

Noise legislations and the increasing customer demands determine the NVH-development of modern commercial vehicles. In this paper suitable engineering approaches will be discussed. In order to meet the very stringent legislative requirements of the EEC and some other countries refinement of all vehicle noise sources is required. Cost-effective solutions, however, can only be found with low-noise powertrains, thus being able to avoid excessive noise packages on the vehicle. There is increasing demand, because modular systems should be ready to power a variety of different trucks and busses and allow for easy servicability. With this focus on powertrain noise, the paper discusses and outlines the technological developments required to achieve sufficient noise reduction which aims towards a 1m engine noise level of 93 dBA measured in an acoustic test cell under rated 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 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. Furthermore the increasing number of vehicle configurations leads to a high variability and degrees of freedom in possible system designs and the control thereof, which can only be handled by a comprehensive tool chain of vehicle system simulation and a generic control system architecture. The required model must comprise all relevant systems of the vehicle (control functionality, cooling system, lubrication system, engine, drive train, HV components etc.). For proper prediction with respect to energy consumption all interactions and interdependencies of those systems have to be taken into consideration, i.e. all energy fluxes (mechanical, hydraulically, electrical, thermal) have to be exchanged among the system boundaries accordingly.

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.

Considering worldwide future emission and CO2-legislation for the Light Commercial Vehicle segment, a wide range of powertrain variants is expected. Dependent on the application use cases all powertrain combinations, from pure Diesel engine propulsion via various levels of hybridization, to pure battery electric vehicles will be in the market. Under this aspect as well as facing differing legal and market requirements, a modular approach is presented for the LCV and SUV Segment, which can be adapted flexibly to meet the different requirements. A displacement range of 2.0L to 2.3L, representing the current baseline in Europe is taken as basis. To best fulfill the commercial boundaries, tailored technology packages, based on a common global engine platform are defined and compared. These packages include engine related technical features for emission- and fuel consumption improvement, as well as electrification measures, in particular 48V-MHEV variants.

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.

Modern safety and comfort features must behave country specific to the local environment and traffic conditions in order to gain end consumers’ trust and strengthening OEMs market success respectively. In order to achieve this, a new methodology was developed. In this paper, the approach for designing advanced driving assistance systems (ADAS) with a tailored controller behavior optimized for country specific market expectations like in India is described. Furthermore, the definition of objective performance and calibration targets with automated evaluation of target fulfillment will be deeply discussed. The method is focused on saving time at calibration and validation without compromising the quality of ADAS features. Local market specific driving behavior is investigated and measurement data from real-world driving collected. Data clustering via maneuver detection is performed automatically, which is saving time and effort.

The gap between the officially reported CO2 values and the actual performance of the vehicle on the road is continuously increasing. Numerous studies are showing differences between the official values and the real-world measurements of more than 40% in average, with further increases year by year. The fuel consumption of passenger cars are determined as part of the vehicle certification according to Euro 6 via carbon mass balance using exhaust gas measurement. By introducing the new world harmonized driving cycle (WLTC) in September 2017, which is addressing a more realistic speed profile or traffic conditions, the gap between the certification and road test is expected to be reduced in half. Additionally the EU Commission plans to monitor vehicles more closely. From 2020, devices for recording fuel and energy consumption will become mandatory in all passenger cars and light commercial vehicles, reflecting the average real world CO2 emissions.

The internal combustion engine contains several actuators to control engine performance and emissions. These are controlled within the engine ECU and follow a specific operating strategy to achieve objectives such as NOx reduction and fuel economy. However, these two goals are conflicting and a compromise is required. The operating state depends on system constraints such as engine speed, load, temperature levels, and aftertreatment system efficiency. This results in constantly changing target values to stay within the defined limits, especially the legal emission limits. The conventional approach is to use multiple operating modes. Each mode represents a specific compromise and is activated accordingly. Multiple modes are required to meet emissions regulations under all required conditions, which increases the calibration effort. This new control approach uses an artificial neural network to replace the conventional multiple mode approach.

India is the market with the highest sales of agricultural tractors and the market with the highest number of agricultural tractor park, as well. Even though taking into account the lower average power of Indian agricultural tractors compared to regions with considerably larger field sizes, their cumulated diesel fuel consumption reaches a significant size. The possible use of alternative powertrains like battery-electric, especially considering the lower power of the Indian tractor market, seems feasible, but might be struggling with challenges in terms of charging infrastructure and the possibly resulting lower productivity due to required charging times. Therefore AVL proposes to investigate the use of alternative fuels for internal combustion engines, a topic which is also being discussed by other global tractor OEMs. In that context the focus is typically on higher tractor powers due to current storage limitations of battery-electric systems and other alternatives.

India striving for carbon neutrality influences futures powertrain architecture of commercial vehicles. The use of CO2-free drives as battery electric have been demonstrated for various applications. The productivity still is a challenge due to missing high power charging infrastructure or limited range. This draws the attention to the use of sustainable fuels due to lower refueling times. The hydrogen engine got highest attention in the last couple of years. For markets as the EU the driver for hydrogen is the CO2 emission reduction, whereas for markets as India hydrogen offers the additional opportunity for more independence from fossil imports. Different OEMs all over the world have converted diesel engines to hydrogen operation with strong focus on performance and emission demonstration, so far with limited technology readiness of different key components.

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).