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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.

At first glance RDE seems to be a road testing topic only, mistakenly. While Type approval test must be performed at the Road and Chassis Dyno, development work beforehand delivers solutions fulfilling the demanded legislation limits. Making the right development steps and decisions will lead to a technical solution within economy of scales. Much of this work done happens on engine testbeds and Real Driving Emissions (RDE) per UN-ECE legislation or the new test cycle for the chassis dyno according WLTP (Worldwide Harmonized Light-Duty Test Procedure) will not change that. The question is, are engine test beds fit for this new challenge or are changes required? One characteristic element of RDE is the randomness of operating conditions generated by a road drive. There must be found a way, to achieve RDE relevant test conditions in a most reproducible manner.

Validation of highly automated or autonomous vehicles is nowadays still a major challenge for the automotive industry. Furthermore, the homologation of ADAS/AD vehicles according to global regulations is getting more essential for their safe development and deployment around the world. In order to assure that the autonomous driving function is able to cope with the huge number of possible situations during operation, comprehensive testing of the functions is required. However, conventional testing approaches such as driving distance-based validation approach in the real world, can be time- and cost-consuming. Therefore, a scenario-based virtual validation and testing method is considered to be a proper solution. In this paper, we propose a virtual assessment framework using a fully automated test case generation method. This framework is embedded into the continuous development and validation process.

Combustion of premixed stoichiometric charge is free of soot particle formation. Consequently, the development of direct injection (DI) spark ignition (SI) engines aims at providing premixed charge to avoid or minimize soot formation in order to meet particle emissions targets. Engine development methods not only need precise engine-out particle measurement instrumentation but also sensors and measurement techniques which enable identification of in-cylinder soot formation sources under all relevant engine test conditions. Such identification is made possible by recording flame radiation signals and with analysis of such signals for premixed and diffusion flame signatures. This paper presents measurement techniques and analysis methods under normal engine and vehicle test procedures to minimize sooting combustion modes in transient engine operation.

Future legislation scenarios as well as stringent CO2 targets, in particular under real driving conditions, will require the introduction of new and additional powertrain technologies. Beside the increasing electrification of the powertrain, it will be essential to utilize the full potential of the Internal Combustion Engine (ICE). There is clearly a competition of new and different ICE-Technologies [1] including VCR. VCR systems are expected to be introduced to a considerable number of next generation turbocharged Spark Ignited (SI) engines in certain vehicle classes. The implementation of Miller or Atkinson cycles is an essential criterion for increased geometric Compression Ratio (CR). The DUAL MODE Variable Compression System (VCS)TM enables a 2-stage variation of the connecting rod length and thus of the compression ratio (CR).

Dynamometer (dyno) durability testing plays a significant role in reliability and durability assessment of commercial engines. Frequently, durability test procedures are based on warranty history and corresponding component failure modes. Evolution of engine designs, operating conditions, electronic control features, and diagnostic limits have created challenges to historical-based testing approaches. A physics-based methodology, known as Load Matrix, is described to counteract these challenges. The technique, developed by AVL, is based on damage factor models for subsystem and component failure modes (e.g. fatigue, wear, degradation, deposits) and knowledge of customer duty cycles. By correlating dyno test to field conditions in quantifiable terms, such as customer equivalent miles, more effective and efficient durability test suites and test procedures can be utilized. To this end, application of Load Matrix to a heavy-duty diesel engine is presented.

One of the most important safety issues for automotive engineering is to avoid any fire due to the ignition of flammable liquids, which may result from leaks. Fire risk is a combination of hot temperature, fast vaporisation and accumulation of vapor in a cavity. In IC engines, potentially flammable liquids are fuel and oil. To guarantee safety, flammable liquids must not come into contact with hot parts of the engine. Consequently, shields are designed to guide the flow path of possible leakages and to take any flammable liquid out of the hot areas. Simulation is a great help to optimize the shape of the shield by investigating a large number of possible leakages rapidly. Recent breakthroughs in numerical methods make it possible to apply simulations to industrial design concepts. The employed approach is based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) method.

In recent years, the use of high-power inverters has become increasingly prevalent in vehicles applications. With the increasing number of electric vehicle models comes the need for efficient and reliable testing methods to ensure the proper functioning of these inverters. One such method is the use of Hardware-in-the-Loop (HiL) environments, where the inverter is connected to a simulated environment to test its performance under various operating conditions. HiL testing allows for faster and more cost-effective testing than traditional methods and provides a safe environment to evaluate the inverter's response to different scenarios. Further, in such an environment, it is possible to specifically stimulate those system states in which conflicts between the lines arise regarding the ideal system parametrization. By combining HiL testing with design-of-experiments and modelling methods, the propulsion system can hence be optimized in a holistic manner.

Increasing requirements for the result quality of exhaust emission analyzers and state of the art analyzer technology require a new point of view regarding measuring range definitions and linearization procedures. To make best use of the power of this analyzer technology, linearization procedures need reconsideration. In certification laboratories, legislation defines the procedures to linearize an exhaust emission analyzer more or less stringently. On the other hand, on testbeds for development purposes there are many possibilities for making use of today's improved analyzers. However, procedures are often used in development labs that are very similar to those mentioned in the legislation. For some measurement purposes it is necessary to leave these procedures regarding measuring ranges and their specifications behind. The exhaust gas analyzing system has to provide consistent result quality during the whole test procedure.

System lubrication in automotive powertrains is a growing topic for development engineers. Hybrid and pure combustion system complexity increases in search of improved efficiency and better control strategy, increasing the number of components with lubrication demand and the interplay between them, while fully electric systems drive for higher input speeds to increase e-motor efficiency, increasing bearing and gear feed rate demands. Added to this, many e-axle and hybrid systems are in development with a shared medium and circuit for e-motor cooling and transmission lubrication. Through all this, the lubricant forms a common thread and is a fundamental component in the system, but no standardized tests can provide a suitable methodology to investigate the adequate lubrication of components at powertrain level, to support the final planned vehicle usage.

In 2017 the European authorities put into effect the first part of a new certification test procedure for Real Driving Emissions (RDE). Similar tests are planned in other regions of the world, such as the upcoming China 6a/6b standards, further tightening emission limits, and also the introduction of RDE tests. Both restrictions pose challenging engineering tasks for upcoming vehicles. RDE certification tests feature significantly more demanding engine operating conditions and thus, emit more pollutants by orders of magnitude compared to known cycles like NEDC. Here, especially the reduction of NOx is a specific technical challenge, as it needs to compromise also with reduction targets on carbon dioxide. The fulfilment of both emission limits requires a widening of the focus from an isolated engine or exhaust aftertreatment view to a system engineering view involving all hardware and software domains of the vehicle.

Fully flexible dual fuel (DF) internal combustion (IC) engines, that can burn diesel and gas simultaneously, have become established among heavy-duty engines as they contribute significantly to lower the environmental impact of the transport sector. In order to gain better understanding of the DF combustion process and establish an effective design methodology for DFIC engines, high fidelity computational fluid dynamics (CFD) simulation tools are needed. The DF strategy poses new challenges for numerical modelling of the combustion process since all combustion regimes have to be modelled simultaneously. Furthermore, DF engines exhibit higher cycle-to-cycle variations (CCV) compared to the pure diesel engines. This issue can be addressed by employing large eddy simulation coupled with appropriate DF detailed chemistry mechanism. However, such an approach is computationally too expensive for today’s industry-related engine calculations.

Modern powertrain noise investigation in the development process and during trouble shooting is a combination of experiment and simulation. In simulation in recent years main focus was set on model completeness, consideration of all excitation mechanisms and efficient and stabile numerical algorithms. By that the total response of the virtual powertrain is already comparable to the overall noise level of the real powertrain. Actual challenge is to trace back the overall response to its main excitation and noise generating mechanism as well as to their main driving parameters to support the engineer not only in reaching absolute values, but also to derive the root cause of a response or potential problem and to get hints on how to improve the specific behavior. Approaches by parameter sensitivity studies are time consuming and not unambiguous.

Direct injection Diesel engines are a propulsion technology that is continuously developed to meet emission standards. Great optimization potential lies in the combustion process itself. The application of closed loop combustion control allows reacting online to environmental conditions and stabilizing the combustion regarding performance and emissions. Dedicated real-time plant models help to develop and calibrate control algorithms in office and hardware in the loop environments. The present work describes a real-time capable, crank-angle resolved engine, cylinder and combustion model. The cylinder applies an 0D, two-zone approach and a phenomenological combustion model describes ignition delay, premixed and diffusive combustion. The latter is enhanced by a quasi-dimensional description of the injection spray. The model is validated with dedicated measurements. The plant model is applied in two use-cases for closed loop combustion control.

Making lighter engines is in the agenda of all OEMs in order to make their cars lighter and to reduce CO2 emission based on regulations. On the other hand, the noise regulations are getting more stringent and the customer impression of interior sounds is still an important aspect in vehicle development. Vehicle noise legislation has been revised numerous times since it was first established in February 1970. The latest revision in EU legislation introduces a revised test method which is used to enforce diminishing noise limits in three phases (EU Regulation No. 540/2014). Since 2016 the noise limit for passenger cars has been 72 dB(A). It will be reduced to 70 dB(A) in 2020 and to 68 dB(A) in 2024. These vehicle pass by noise limits cascade down to limitations on engine noise. New engine designs face a trade-off between a lightweight design and fulfilling the NVH targets. The conventional design updates are done by adding ribs and usually mass to the engine.

Traditionally, Electric Machine design has primarily focused on factors like efficiency, packaging, and cost, often neglecting the critical aspects of Noise, Vibration, and Harshness (NVH) in the early decision-making stages. This disconnect between E-Machine design teams and NVH teams has consistently posed a challenge. This paper introduces an innovative workflow that unifies these previously separate domains, facilitating comprehensive optimization by seamlessly integrating NVH considerations with other E-Machine objectives, such as electromagnetic compatibility (EMC). This paper highlights AVL's approach in achieving this transformation and demonstrates how this integrated approach sets a new standard for E-Machine design. The presented approach relies on AI-driven algorithms and computational tools.

Paper outlines a case study on optimal control of a brushless direct-current (BLDC) motor, operating as an Integrated Starter Generator (ISG) in a micro hybrid propulsion system for motorcycles. Main research focus is optimization of BLDC machine torque characteristics, particularly in starter operation mode, in order to improve cranking of the internal combustion engine (ICE) at various operation conditions. Stringent cranking torque demands, limited physical dimensions of the electrical machine and a wide rotational speed range of prototype ICE are most challenging reasons for the exhaustive study of applicable control algorithms in the low rpm range. Two approaches for optimization of torque characteristics are discussed, common known flux-weakening method and the modification of power-switch conduction angle, respectively. The evaluation of most relevant control approaches is based on computer simulation and prototype set-up measurements.