Your Destination for Mobility Engineering Resources

Announcements for SAE Mobilus

Browse All

Recent SAE Edge™ Research Reports

Browse All 171

Recent Books

Browse All 698

Recently Published

Browse All
The increasing complexity of modern vehicles and the automotive industry's shift towards Software Defined Vehicles (SDVs) require innovative solutions to streamline development processes. Traditional methods of software development often struggle to meet the demands for agility, scalability, and precision in this context. In response, this paper presents a novel approach utilizing Artificial Intelligence (AI), specifically Large Language Models (LLMs), to automate the generation of executable code directly from Systems Engineering (SE) specifications. This novel approach aims to transform how SE requirements are converted into implementation-ready code, reducing the inefficiencies and potential errors associated with manual translation. LLMs trained on domain-specific data are capable of interpreting complex requirements, managing dependencies, and generating consistent and accurate code. By integrating LLMs into the automotive software pipeline, companies can improve productivity
Padubrin, MarcelReuss, Hans-ChristianBrosi, FrankMenz, LeonhardGuerocak, Erol
Vehicles are prime examples of cyber-physical systems that rely on multiple domains, including mechanics, electronics, and software. Due to high customizability and software changes introduced by bug fixes or functional upgrades, vehicle instances vary in space (variants) and time (versions). This results in a huge number of possible variants and versions; thus, testing all combinations to ensure functional safety is practically infeasible. Moreover, components of all domains interact with each other; thus, solely focusing on single domains while testing multi-domain cyber-physical systems is insufficient. In this paper, we propose a process for change-aware testing of cyber-physical systems, including test activities we identified during a literature analysis. The process consists of multiple structured steps, including the selection of affected variants, test case selection, and adaptive configuration of test environments. Based on the process and identified activities, we discuss
Beck, MaximilianBirkemeyer, LukasPett, TobiasUrbano, FrancescoBause, KatharinaAlbers, AlbertSax, EricSchaefer, Ina
Electrification of city busses is an important factor for decarbonisation of the public transport sector. Due to its strictly scheduled routes and regular idle times, the public transport sector is an ideal use case for battery electric vehicles (BEV). In this context, the thermal management has a high potential to decrease the energy demand or to increase the vehicles range. The thermal management of an electric city bus controls the thermal behaviour of the components of the powertrain, such as motor and inverters, as well as the conditioning of the battery system and the heating, ventilation, and air conditioning (HVAC) of the drivers’ front box and the passenger room. The focus of the research is the modelling of the thermal behaviour of the important components of an electric city bus in MATLAB/Simscape including real-world driving cycles and the thermal management. The heating of the components, geometry and behaviour of the cooling circuits as well as the different mechanisms of
Schäfer, HenrikMeywerk, MartinHellberg, Tobias
Steer-by-wire actuators represent a transformative advancement in chassis control, opening up new potential for optimizing driving behavior across the entire range of driving dynamics - including driver-dependent automatic counter steering in critical driving situations. However, from a functional safety perspective, the increased potential also introduces new risks with respect to possible system failures. To mitigate these risks, sophisticated monitoring functions are essential to ensure vehicle controllability at all times. Current research approaches for monitoring functions use safe driving envelopes. This set of safe driving states is often found by open-loop simulations, which provide a phase portrait of the nonlinear system under control and from which stability limits can be derived. However, it remains open how these open-loop stability limits correspond to the stabilization capability of a real human driver in the loop. And secondly, how these closed-loop stability limits
Birkemeyer, JanickNaidu P.M, TarunBorkowski, LukasMüller, Steffen
The automotive industry is undergoing a major shift from internal combustion engines to hybrid and battery electric vehicles, which has led to significant advancements and increased complexity in drivetrain design and thermal management systems. This complexity reflects the growing need to optimize energy efficiency, extend vehicle range, and ensure system reliability in modern electric vehicles. At the Institute of Automotive Engineering, a specialized synthesis tool for drivetrain optimization is used to identify the best drivetrain configurations based on specific boundaries and requirements. Building up on this toolchain a modular and adaptable thermal management framework has been developed, addressing another critical aspect of vehicle and drive development: efficient thermal circuit layout and its impact on energy consumption and overall system reliability. The thermal framework emphasizes the dynamic interactions between key components, such as electric machines, power
Notz, FabianSturm, AxelSander, MarcelKässens, ChristophHenze, Roman
The design, development, and optimization of modern suspension systems is a complex process that encompasses several different engineering domains and disciplines such as vehicle dynamics simulation, tire data analysis, 1D lap-time simulation, 3D CAD design and structural analysis including full 3D collision detection. Typically, overall vehicle design and suspension development are carried out in multiple iterative design loops by several human specialists from diverse engineering departments. Fully automating this iterative design process can minimize manual effort, eliminate routine tasks and human errors, and significantly reduce design time. This desired level of automation can be achieved through digital modeling, automated model generation, and simulation using graph-based design languages and an associated language compiler for translation and execution. Graph-based design languages ensure the digital consistency of data, the digital continuity of processes, and the digital
Borowski, JulianRudolph, Stephan
The optimization and further development of automated driving functions offer significant potential for reducing the driver's workload and increasing road safety. Among these functions, vehicle lateral control plays a critical role, especially with regard to its acceptance by end customers. Significant development efforts are required to ensure the effectiveness and reliability of this aspect in real-world conditions. This work focuses on analyzing lateral vehicle control using extensive measurement data collected from a dedicated vehicle fleet at the Institute of Automotive Engineering at the Technical University of Braunschweig. Equipped with state-of-the-art measurement technology, the fleet has driven several hundred thousand kilometers, allowing for the collection of detailed information on vehicle trajectories under various driving conditions. A total of 93 participants, aged between 20 and 43 years, contributed to the dataset. These measurements have been classified into
Iatropoulos, JannesPanzer, AnnaArntz, MartinPrueggler, AdrianHenze, Roman
The lateral movement of vehicles within their lane determines occurring occlusions, and thus decides on the range of vision of vehicle sensors used by automated driving functions, and the objects detected by down-stream algorithms. As simulations play an integral role in the validation of automated driving functions, their ability to realistically model the lateral movement is crucial. However, currently applied methods such as microscopic traffic simulations, and scenario-based testing making use of maneuver-based scenario descriptions simplify or neglect the lateral movement of vehicles. For that reason, a two-level stochastic model has been introduced in earlier work. It consists of a Markov model for the systematic coarse movement, and a noise model for the residual fine movement. In follow-up publications, several advancements for both model components have been presented. These follow a modular structure, thus, can be flexibly combined. This paper for the first time gives an
Neis, NicoleBeyerer, Jürgen
With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles’ position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train
Flormann, MaximilianMeyer, FelixHenze, Roman
In the context of the clean transport sector, there has been growing interest in the use of hydrogen in internal combustion engines due to its potential to nearly eliminate all engine-out criteria pollutants, while maintaining high thermal efficiency through the use of a lean combustion process. In direct injection configurations, mixing process is significantly influenced by hydrogen jet dynamics. First, a comprehensive experimental campaign was conducted in a constant volume vessel to assess the performance of a hydrogen injector using the Schlieren technique. The jet behavior was analyzed by varying injector recess, injection pressure, and back pressure. Subsequently, the case study was replicated in a 3D Computational Fluid Dynamics (CFD) environment, addressing the complexities associated with modeling under-expanded jets. The model was first validated against experimental data, both in terms of jet morphology and through three geometric indices. Then, a simplified simulation
Pucillo, FrancescoPiano, AndreaMillo, FedericoGiordana, SergioRapetto, NicolaVargiu, Luca
This paper examines the impact of the distribution of charging and hydrogen refueling stations on their reachability for craft vehicles with a defined usage profile. A simulation-based methodology is presented for this purpose. The simulation models daily trips for craft vehicles, considering amongst others the company location, the client stops, the operating radius and the mean daily driving distance. Based on these inputs, the number of charging or refueling opportunities for typical daily trips of the craft vehicle is calculated. To investigate the impact of locations on the frequency of encountering energy provisions, simulations are conducted in three regions: Ulm (urban), Stuttgart (metropolitan), and Munderkingen (rural). Furthermore, the impact of different locations within the same infrastructural area is examined by assessing multiple company locations in Ulm. The findings indicate that the urban zone of Ulm is characterized by a highly dense electric fast charging
Heilmann, OliverMüller, JulianHeinrich, MarcoCortès, SvenSchlick, MichaelKulzer, André Casal
Pre-chambers, in general, represent an established technology for combustion acceleration by increasing the available ignition energy. Realizing rapid fuel conversion facilitates mixture dilution extension with satisfying combustion stability. More importantly, knock-induced spark retarding can be circumvented, thus reducing emissions and increasing efficiency at high engine loads. Adapted valve actuation and split injections were investigated for this study to enhance the gas exchange of a passive pre-chamber igniter in a single-cylinder engine. The findings support the development of passive pre-chamber ignition systems operable over the whole engine map for passenger vehicles. There are two configurations of pre-chamber igniters: passive pre-chambers and scavenged pre-chambers. This study focuses on the passive design, incorporating an additional small volume around the spark plug into the cylinder head. Hot jets exit this volume after the ignition onset through several orifices
Fellner, FelixHärtl, MartinJaensch, Malte
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road
Pohl, EricScheibe, SebastianMünster, MarcoOsebek, ManuelKopp, GerhardSiefkes, Tjark
Computer-aided synthesis and development tools are essential for discovering and optimizing innovative concepts. Evaluating different concepts and making informed decisions relies heavily on accurate assessments of drive system properties. Estimating these properties in the early stages of development is challenging due to the depth of modelling required. In addition, defined requirements play a critical role in drive system sizing. This paper presents a tool chain for the synthesis of new electrified drive concepts, with emphasis on requirements definition and modelling. The requirements definition method combines market analysis with a generalized calculation and estimation approach, providing a novel perspective. In addition, we introduce mass and cost modelling capabilities integrated into the tool chain. The mass model achieves high accuracy, with deviations of only 1.6 % at the vehicle level and 6.1 % at the component level. Finally, the paper examines the mass and cost
Sturm, AxelHenze, Roman
This paper presents an optimisation approach for rotor skewing in a Yokeless and Segmented Armature (YASA) design Axial Flux Machine (AFM) for electric vehicle applications. Torque ripple amplitudes are a critical factor influencing the noise, vibration and harshness (NVH) behaviour of electric motors. The focus of this paper is to reduce the torque ripple amplitudes of the dominant harmonics over the entire torque-speed characteristic of the AFM. The principle of the proposed approach is a segmented permanent magnet configuration of the AFM, where individual magnet segments can be circumferentially shifted to achieve optimal skewing configurations. Initial optimisations are performed using 2D finite element (FE) simulations, modelled as linear motors with multiple slices and different numbers of magnet segmentation. However, the accuracy of the 2D FE results is limited due to the lack of interaction between the individual segments and the insufficient representation of three
Müller, KarstenMaisch, HannesDe Gersem, HerbertBurkhardt, Yves
In order to comply with increasingly stringent emission regulations and ensure clean air, wall-flow particulate filters are predominantly used in exhaust gas aftertreatment systems of combustion engines to remove reactive soot and inert ash particles from exhaust gases. These filters consist of parallel porous channels with alternately closed ends, effectively separating particles by forming a layer on the filter surface. However, the accumulated particulate layer increases the pressure drop across the filter, requiring periodic filter regeneration. During regeneration, soot oxidation breaks up the particulate layer, while resuspension and transport of individual agglomerates can occur. These phenomena are influenced by gas temperature and velocity, as well as by the dispersity and reactivity of the soot particles. Renewable and biomass based fuels can produce different types of soot with different reactivities and dispersities. Therefore, this study focuses on the influences of soot
Desens, OleHagen, Fabian P.Meyer, JörgDittler, Achim
Experimental testing in automotive development sometimes relies on ad hoc approaches like ‘One Factor at a Time’, particularly in time- and resource-limited situations. While widely used, these approaches are limited in their ability to systematically capture parameter interactions and system complexities, which poses significant challenges in safety-critical applications like high-voltage battery systems. This study systematically investigates the factors influencing thermal runaway in lithium-ion battery cells using a statistical full-factorial experimental design. Key parameters, including state of charge, cell capacity and heating trigger power, have been analyzed under controlled conditions with an autoclave setup, enabling precise measurement of thermal and mechanical responses. The use of automotive-grade lithium-ion cells ensures relevance for next-generation applications. By employing factorial regression and statistical analysis, the study identifies critical temperatures
Ceylan, DenizKulzer, André CasalWinterholler, NinaWeinmann, JohannesSchiek, Werner
Ongoing research and development in the field of electric vehicles (EVs) have resulted in a continuous expansion of their range. Additionally, advancements in vehicle connectivity have created new opportunities for intelligent driving assistance and energy optimization, particularly through the use of cloud data. However, the integration of eco-driving assistance with numerical optimization of speed trajectories remains challenging due to the high computational demands of these methods. To address this challenge and make such a system feasible for integration into vehicle systems, the computational effort required for an optimized driving trajectory must be minimized. This paper presents a method to accelerate speed trajectory optimization using pre-calculated energy and time consumption maps. For this purpose, a dynamic discretization of the anticipated driving profile is applied. Initial results show a substantial reduction in computation time, varying with different scenarios
Schilling Johnson, ReneHenke, Markus
Internal combustion engines generate higher exhaust emissions of hazardous gases during the initial minutes after engine start. Experimental data from a state-of-the-art turbo-charged 3-cylinder, 999 cc gasoline engine are used to predict cold start emissions using two Machine Learning (ML) models: a Multilayer Perceptron (MLP) which is a fully connected neural network and an Encoder-Decoder Recurrent Neural Network (ED-RNN). Engine parameters and various temperatures are used as input for the models and NOx (Nitrogen Oxides), CO (Carbon monoxide) and unburned hydrocarbon (UHC) emissions are predicted. The dataset includes time series recordings from the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) and four Real Diving Emissions (RDE) cycles at ambient and initial engine temperatures ranging from -20 °C to +23 °C. In total, 21 cases are considered, consisting of eight different ambient temperatures and five distinct driving cycles. Each case consists of a sequence of 2500
Mangipudi, ManojDenev, Jordan A.Bockhorn, HenningTrimis, DimosthenisKoch, ThomasDebus, CharlotteGötz, MarkusZirwes, ThorstenHagen, Fabian P.Tofighian, HesamWagner, UweBraun, SamuelLanzer, TheodorKnapp, Sebastian M.
Fast charging of lithium-ion batteries presents significant thermal management challenges, due to the high demanding conditions of high C-rates, particularly at extreme ambient temperatures. This study explores the thermal behavior of a cylindrical lithium-ion cell during fast-charging scenarios designed to achieve a full charge in 15 minutes or less (SOC: 0%–100%), across a wide range of ambient temperatures. The analysis covers a broad spectrum of ambient temperatures, from 303 K to 333 K, addressing real-world operational challenges faced by electric vehicles and energy storage systems. A validated thermal model, calibrated with experimental data on the open circuit voltage (OCV) and internal resistance of the cell across varying conditions, is employed to accurately predict the temperature distribution of the cell at different states of charge (SOC). The model also includes scenarios involving high initial cell temperatures to assess their effect on thermal performance during fast
Jahanpanah, JalalMahmoudzadeh Andwari, AminBabaie, MeisamKonno, JuhoAkbarzadeh, Mohsen
To achieve a significant reduction in net CO₂ emissions in the aviation sector, sustainable aviation fuels (SAFs) are considered a key factor. Current research efforts are therefore focused on SAFs, which exhibit properties that differ from conventional kerosene, particularly in aspects critical to compression-ignition (CI) engines, such as cetane number, evaporation behavior or lubricity. These differences necessitate dedicated investigations to assess their suitability and performance in such engines. However, real operating conditions — such as intake air- and exhaust- pressure levels during flight — cannot be fully replicated on standard engine test benches. For this reason, real flight experiments were conducted to address these limitations. Notably, this work marks the first instance of in-flight testing of SAFs in CI aviation engines, constituting a significant milestone in this research area. In the course of these investigations, ASTM D7566 Annex A2-compliant HEFA
Kleissner, FlorianReitmayr, ChristianHofmann, Peter
Electric vehicles are no longer a rarity on Europe’s streets. But battery electric vehicles (BEVs) still have a long way to go to be the dominant vehicle type on the streets. In the last years, not only has the number of passenger cars risen, but also the number of electric trucks and heavy-duty vehicles. In 2023 electric trucks have share of 1.5% in the market. [1, 2] For the truck industry higher charging powers are even more important. Due to European regulations drivers of vehicles with more than 3.5t weight or buses with more than 10 passengers must rest for 45 minutes after 4.5 hours of drive. [3] Therefore, higher charging powers were needed, and the Megawatt Charging System (MCS) standard was developed. The voltage level goes up to 1250 V and currents of 3000 A are defined. [4] This allows the battery of heavy-duty vehicles to be completely charged within the driving breaks. As with the upcoming MCS standard, the charging power increases, also the failure risk rises. Higher
Grund, CarolineReuss, Hans-Christian
Human driver errors, such as distracted driving, inattention, and aggressive driving, are the leading causes of road accidents. Understanding the underlying factors that contribute to these behaviors is critical for improving road safety. Previous studies have shown that physiological states, like raised heart rates due to stress and anxiety, can influence driving behavior, leading to erratic driving and an increased risk of accidents. In this study, we conducted on-road tests using a measurement system based on the Driver-Driven vehicle-Driving environment (3D) method. We collected physiological signals, specially electrocardiography (ECG) data, from human drivers to examine the relationship between physiological states and driving behaviors. The aim was to determine whether ECG can serve as an indicator of potential risky driving behaviors, such as sudden acceleration and frequent steering adjustments. This information enables automated driving (AD) systems to intervene in dangerous
Ji, DejieFlormann, MaximilianBollmann, JulianHenze, RomanDeserno, Thomas M.
Vehicles are evolving into Software-Defined Vehicles. The increasing use of automotive High Performance Computers (HPCs) provides more computing power and storage resources in vehicles. This opens possibilities to use more in-vehicle software. However, it also leads to challenges for vehicle diagnostics. Today's diagnostic approaches, based on Diagnostic Trouble Codes (DTCs), are not suitable for software on HPCs. For example, this software is highly variable and updated over time, so predefined DTCs are not dynamic enough. This introduces a degree of ambiguity into the diagnostic processes. Additional diagnostic data are required. In the Cloud, observability approaches are becoming widely used for software. Observability involves examining the availability and performance of an entire software system. To detect failures early, observability data, such as logs, metrics, and traces, are used. This is of interest for vehicle diagnostics as new diagnostic approaches are needed to
Bickelhaupt, SandraHahn, MichaelWeyrich, MichaelMorozov, Andrey
In the automotive development process objective criteria are commonly used to evaluate the full vehicle ride comfort of vehicles. Based on these characteristics, vehicle concepts can be evaluated and compared at an early stage without using physical prototypes. Usually, these characteristics are determined in subjective studies using real vehicles. However, limitations in the implementation of vehicle variants, the controllability of external influences and longer intervals between the individual assessments have a negative impact on the quality of results using these approaches. Therefore, this paper presents an improved method to transfer the subjective perception and evaluation of ride comfort phenomena to objective characteristics. The corresponding procedure is shown on the basis of a one-dimensional, periodic phenomenon that is transferred to a frequency-dependent weighting function. In this process, a 6-degree of freedom driving simulator is used to overcome the limitations
Stroesser, SimonAngrick, ChristianZwosta, TobiasNeubeck, JensWagner, Andreas