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Vehicle functional requirements, emission regulations, and thermal limits all have a direct impact on the design of a powertrain cooling airflow system. Given the expected increase in emission-related heat rejection, suppliers and vehicle manufacturers must work together as partners in the design, selection, and packaging of cooling system components. The goal of this two-day course is to introduce engineers and managers to the basic principles of cooling airflow systems for commercial and off-road vehicles.

Multiple three-phase machines have become popular in recent due to their reliability, especially in the ship and airplane propulsions. These systems benefit greatly from the robustness and efficiency provided by such machines. However, a notable challenge presented by these machines is the growth of harmonics with an increase in the number of phases, affecting control precision and inducing torque oscillations. The phase shift angles between winding sets are one of the most important causes of harmonics in the stator currents and machine torque. Traditional approaches in the study of triple-three-phase or nine-phase machines mostly focus on specific phase shift, lacking a comprehensive analysis across a range of phase shifts. This paper discusses the current and torque harmonics of triple-three-phase permanent magnet synchronous machines (PMSM) with different phase shifts. It aims to analyze and compare the impacts of different phase shifts on harmonic levels.

Nowadays, green hydrogen can play a crucial role in a successful clean energy transition, thus reaching net zero emissions in the transport sector. Moreover, hydrogen exploitation in internal combustion engines is favoured by its suitable combustion properties and quasi-zero harmful emissions. High flame speeds enable a lean combustion approach, which provides high efficiency and reduces NOx emissions. However, high air flow rates are required to achieve the load levels typical of heavy-duty applications. In this framework, the present study aims to investigate the required boosting system of a 6-cylinder, 13-liter heavy-duty spark ignition engine through 1D numerical simulation. A comparison among various architectures of the turbocharging system and the size of each component is presented, thus highlighting limitations and potentialities of each architecture and providing important insights for the selection of the best turbocharging system.

The hydrogen engine is one of the promising technologies that enables carbon-neutral mobility, especially in heavy-duty on- or off-road applications. In this paper, a methodological procedure for the design of the combustion system of a hydrogen-fueled, direct injection spark ignited commercial vehicle engine is described. In a preliminary step, the ability of the commercial 3D computational fluid dynamics (CFD) code AVL FIRE classic to reproduce the characteristics of the gas jet, introduced into a quiescent environment by a dedicated H2 injector, is established. This is based on two parts: Temporal and numerical discretization sensitivity analyses ensure that the spatial and temporal resolution of the simulations is adequate, and comparisons to a comprehensive set of experiments demonstrate the accuracy of the simulations. The measurements used for this purpose rely on the well-known schlieren technique and use helium as a safe substitute for H2.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

In order to improve the efficiency of passenger cars, developments focus on decreasing their aerodynamic drag, part of which is caused by cooling air. Thus, car manufacturers try to seal the cooling air path to prevent leakage flows. Nevertheless, gaps between the single components of the cooling air path widen due to the deformation of components under aerodynamic load. For simulating the cooling airflow utilization ratio (CAUR), computational fluid dynamics (CFD) simulations are used, which neglect component deformation. In this paper, a computational method aiming at sufficient gap resolution and determining the CAUR of passenger cars under the consideration of component deformation is developed. Therefore, a partitioned approach of fluid structure interaction (FSI) simulations is used. The fluid field is simulated in OpenFOAM, whereas the structural simulations are conducted using Pam-Crash.

In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.

Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.

The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

Reducing CO2 emissions in on-the-road transport is important to limit global warming and follow a green transition towards net zero Carbon by 2050. In a long-term scenario, electrification will be the future of transportation. However, in the mid-term, the priority should be given more strongly to other technological alternatives (e.g., decarbonization of the electrical energy and battery recharging time). In the short- to mid-term, the technological and environmental reinforcement of ICEs could participate in the effort of decarbonization, also matching the need to reduce harmful pollutant emissions, mainly during traveling in urban areas. Engine thermal management represents a viable solution considering its potential benefits and limited implementation costs compared to other technologies. A variable flow coolant pump actuated independently from the crankshaft represents the critical component of a thermal management system.

Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.

Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The novel method is based on non-linear theories in the frequency domain. It uses the harmonic balance method in combination with the alternating frequency/time domain method to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked.

Ammonia has emerged as a promising carbon-free alternative fuel for internal combustion engines (ICE), particularly in large-bore engine applications. However, integrating ammonia into conventional engines presents challenges, prompting the exploration of innovative combustion strategies like dual-fuel combustion. Nitrous oxide (N2O) emissions have emerged as a significant obstacle to the widespread adoption of ammonia in ICE. Various studies suggest that combining exhaust gas recirculation (EGR) with adjustments in inlet temperature and diesel injection timing can effectively mitigate nitrogen oxides (NOx) emissions across diverse operating conditions in dual-fuel diesel engines.

Centrifugal fans are applied in many industrial and civil applications, such as manufacturing processes and building HVAC systems. They can also be found in automotive applications. Noise-reduction mea- sures for centrifugal fans are often challenging to establish, as acous- tic performance may be considered a tertiary purchase criterion after energetic efficiency and price. Nonetheless, their versatile application raises the demand for noise control. In a low-Mach-number centrifugal fan, acoustic waves are predominantly excited by aerodynamic fluctu- ations in the flow field and transmit to the exterior via the housing and duct walls. The scientific literature documents numerous mech- anisms that cause flow-induced sound generation, even though only some are considered well-understood. Numerical simulation methods are widely used to gather spatially high-resolved insights into physical fields.

With the electrification of powertrains, the noise level inside vehicles reach high levels of silence. The dominant engine noise found in traditional vehicles is now replaced by other sources of noise such as rolling noise and aeroacoustic noise. These noises are encountered during driving on roads and highways and can cause significant fatigue during long journeys. Regarding aeroacoustic phenomena, the noise transmitted into the cabin is the result of both turbulent pressure and acoustic pressure created by the airflow. Even though it is lower in level, the acoustic pressure induces most of the noise perceived by the occupants. Its wavelength is closer to the characteristic vibration wavelengths of the glass, making its propagation more efficient through the vehicle's windows. The accurate modeling of these phenomena requires the coupling of high-frequency computational fluid dynamics (CFD) simulations and vibro-acoustic simulations.

The development of an effective Acoustic Vehicle Alert System (AVAS) is not solely about adhering to safety regulations; it also involves crafting an auditory experience that aligns with the expectations of vulnerable road users. To achieve this, a deep understanding of the acoustic transfer function is essential, as it defines the relationship between the sound emitter (the speaker inside the vehicle) and the receiver (the vulnerable road user). Maintaining the constancy of this acoustic transfer function is paramount, as it ensures that the sound emitted by the vehicle aligns with the intended safety cues and brand identity that is defined by the car manufacturer. In this research paper, three distinct methodologies for calculating the acoustic transfer function are presented: the classical Boundary Element method, the H-Matrix BEM accelerated method, and the Ray tracing method.

Though modal analysis is a common tool to evaluate the dynamic properties of a structure, there are still many individual decisions to be made during the process which are often based on experience and make it difficult for occasional users to gain reliable and correct results. One of those experience-based choices is the correct number and placement of reference points. This decision is especially important, because it must be made right in the beginning of the process and a wrong choice is only noticeable in the very end of the process. Picking the wrong reference points could result in incomplete modal analysis outcomes, as it might make certain modes undetectable, compounded by the user's lack of awareness about these missing modes. In the paper an innovative approach will be presented to choose the minimal number of mandatory reference points and their placement.