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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.

The advent of digitalization opens up new avenues for advances in large internal combustion engine technology. Key engine components are becoming "intelligent" through advanced instrumentation and data analytics. By generating value-added data, they provide deeper insight into processes related to the components. An intelligent common rail diesel fuel injection valve for large engine applications in combination with machine learning allows reliable prediction of key combustion parameters such as maximum cylinder pressure, combustion phasing and indicated mean effective pressure. However, fault-related changes to the injection valve also have to be considered. Based on experiments on a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 liter, this study investigates the extent to which the intelligent injection valve can improve the reliability of combustion parameter predictions in the presence of injection valve faults.

The parameterization of the electrochemical pseudo-two-dimensional (P2D) model plays an important role as it determines the acceptance and application range of subsequent simulation studies. Electrochemical impedance spectroscopy (EIS) is commonly applied to characterize batteries and to obtain the exchange current density and the solid diffusion coefficient of a given electrode material. EIS measurements performed with frequencies ranging from 1 MHz down to 10 mHz typically do not cover clearly isolated solid state diffusion processes of lithium ions in positive or negative electrode materials. To extend the frequency range down to 10 μHz, the distribution function of relaxation times (DRT) is a promising analysis method. It can be applied to time-domain measurements where the battery is excited by a current pulse and relaxed for a certain period.

Modeling of lithium iron phosphate electrodes calls for appropriate extensions of established model approaches. An electrochemical pseudo two-dimensional and a single-particle model are enhanced to address the phase separating behavior of this material with a variable solid state diffusion model. A particle size distribution model tackles the heterogeneity of the electrode microstructure. Both models are embedded in a framework to describe multi-layer electrode designs featuring segregated material properties. The models are parameterized following literature replicating a good match with measured discharge curves at low, medium and high currents. A simplified version of the rigorous model shows the effort of reparameterization, the computational advantage of model order reduction techniques, the model accuracy and application scope.

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.

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.

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.

This work elaborates the transferability of electrode diffusion coefficients gained from fitting procedures in frequency domain to an electrochemical battery model run in time domain. An electrochemical battery model of an NMC622 half-cell electrode is simulated with sinusoidal current excitations at different frequencies. The current and voltage signals are analyzed in frequency domain via Nyquist and Bode plots. The frequency domain analysis of time domain simulations is applied to assess the numerical convergence of the simulation and the sensitivity on particle diameter, electrode and electrolyte diffusion coefficients. The simulated frequency spectra are used to fit the electrode diffusion coefficient by means of different electrical equivalent circuit models and the electrochemical battery model itself. The fitted diffusion coefficients from the different electrical equivalent circuit models deviate by one order of magnitude from the a priori known reference data.

As electrified powertrains trends towards the new norm in development, the need to consider modular development approaches becomes more prevalent. Modular system developments seek to offer an adaptable product range by considering each system component (transmission, e-motor, inverter, battery, etc.) and system element (park-lock, disconnect, differential, etc.) as interchangeable. This can result in a lower cost development process overall to increase the returns for tier1 suppliers by expanding the marketability of the platform. Such an approach has hitherto held relatively low commercial interest as the rate of technological advancement negated the benefits of a modular development due to the lack of long-term competitivity. Previously large technological advances between successive productions and the relatively limited EV market, centred around SUV and small car applications, reduced the value in committing to a platform development.

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.

Minimizing the lubricant volume in a transmission system reduces the churning losses and overall unit costs. However, lubricant volume reduction is also detrimental to the thermal stability of the system. Transmission overheating can result in significant issues in the region of loaded contacts, risking severe surface/sub-surface damage in bearings and gears, as well as reduction in the lubricant quality through advanced oxidation and shear degradation. The increasing trend of electrified transmission input speeds raises the importance of understanding the thermal limits of the system at the envelope of the performance to ensure quality and reliability can be maintained, as well as being a key factor in the development, effecting internal housing features for the promotion of lubrication. A nodal bearing thermal model will be shown which utilizes thermal resistances and smooth particle based CFD for determining bearing lubricant feed rates during operation.

The shift toward electrification and limitations in battery electric vehicle technology have led to high demand for hybrid vehicles (HEVs) that utilize a battery and an internal combustion engine (ICE) for propulsion. Although HEVs enable lower fuel consumption and emissions compared to conventional vehicles, they still require combustion of fuels for ICE operation. Thus, emissions from hybrid vehicles are still a major concern. Engine starts are a major source of emissions during any driving event, especially before the three-way catalyst (TWC) reaches its light-off temperature. Since the engine is subjected to multiple starts during most driving events, it is important to mitigate and better understand the impact of these emissions. In this study, experiments were conducted to analyze engine starts in a hybrid powertrain on different experimental setup.

Fast charging of batteries at cold conditions faces the challenge of promoting undesired cell degradation phenomena such as lithium plating. The occurrence of lithium plating is strongly related to local surface potentials and temperatures involving the scales of the electrode surface, the unit cell and the entire module or pack. A multi-scale, multi-domain model is presented, enhancing a Newman based unit cell model with consistent models for heat generation and lithium plating and integrating this 1D+1D approach into a thermal 3D model on module level. The basic equations are presented and three different plating models from literature are discussed. The thermal model is assessed in open-loop simulations and the different plating approaches are compared in charge/discharge simulations at different operating conditions. The full multi-scale, multi-domain model is applied as a virtual sensor for model-based control of fast charging at cold conditions.

E-mobility is a game changer for the automotive domain. It promises significant reduction in terms of complexity and in terms of local emissions. With falling prices and recent technological advances, the second generation of electric vehicles (EVs) that is now in production makes electromobility an affordable and viable option for more and more transport mission (people, freight). Still, major challenges for large scale deployment remain. They include higher maturity with respect to performance (e.g., range, interaction with the grid), development efficiency (e.g., time-to-market), or production costs. Additionally, an important market transformation currently occurs with the co-development of automated driving functions, connectivity, mobility-as-a-service. New opportunities arise to customize road transportation systems toward application-driven, user-centric smart mobility solutions.

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.

Today’s automotive industry is changing rapidly towards environmentally friendly vehicle propulsion systems. All over the globe, legislative CO2 consumption targets are under discussion and partly already in force. Hybrid powertrain configurations are capable to lower fuel consumption and limit pollutant emissions compared to pure IC-Engine driven powertrains. Depending on boundary conditions a numerous of different hybrid topologies- and its control strategies are thinkable. Typical approach is to find the optimum hybrid layout and strategy, by performing certain technical design tasks in office simulation directly followed by vehicle prototype tests on the chassis dyno and road. This leads to a high number of prototype vehicles, overload on chassis dynos, time consuming road test and finally to tremendous costs. Our developed approach is using the engine testbed with simulation capabilities as bridging element between office and vehicle development environment.

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.

Calibrating a vehicle’s powertrain for dynamic operation needs to focus on efforts to mitigate the risks of thermal overload which may arise in the stator or rotor components of an e-motor. Risks also may arise for expected NVH or durability targets, with torque and torque “oscillations” acting as primary sources for the vehicles’ NVH behavior. Both topics, temperature measurement of stator and rotor as well as dynamic torque measurements of the powertrain’s drive shaft are addressed with examples demonstrating the sensors applications in normal test bed and vehicle configurations.

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.

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.