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The UN R155 regulation is the first automotive cyber security regulation and has made security a mandatory approval criterion for new vehicle types. This establishes internationally harmonized security requirements for market approval. As a result, the application of the regulation presents manufacturers and suppliers with the challenge of demonstrating compliance. At process level the implementation of a Cyber Security Management System (CSMS) is required while at product level, the Threat Assessment and Risk Analysis (TARA) forms the basis to identify relevant threats and corresponding mitigation strategies. Overall, an issued type approval is internationally recognized by the member states of the UN 1958 Agreement. International recognition implies that uniform assessment criteria are applied to demonstrate compliance and to decide whether security efforts are sufficient.

The conventional process of last-mile delivery logistics often leads to safety problems for road users and a high level of environmental pollution. Delivery drivers must deal with frequent stops, search for a convenient parking spot and sometimes navigate through the narrow streets causing traffic congestion and possibly safety issues for the ego vehicle as well as for other traffic participants. This process is not only time consuming but also environmentally impactful, especially in low-emission zones where prolonged vehicle idling can lead to air pollution and to high operational costs. To overcome these challenges, a reliable system is required that not only ensures the flexible, safe and smooth delivery of goods but also cuts the costs and meets the delivery target.

Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.

The global time that is propagated and synchronized in the vehicle E/E architecture is used in safety-critical, security-critical, and time-critical applications (e.g., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384. These failures reduce the vehicle availability, robustness, and safety of the road user. IEEE 1588 lists four mechanisms (integrated security mechanism, external security mechanism, architectural solution, and monitoring & management) to secure the global time. AUTOSAR defines the architecture and detailed specifications for the integrated security mechanism "Secured Global Time Synchronization (SGTS)" to secure the global time on automotive networks (CAN, FlexRay, Ethernet).

The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. Due to the short lifespan of software, it becomes necessary to keep these cloud environments and their applications up to date with security updates and new features. However, as new behavior is introduced to the system, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

With the automotive industry's increasing focus on electromobility and the growing share of electric cars, new challenges are arising for the development of electric motors. The requirements for torque and power of traction motors are constantly growing, while installation space, costs and weight are increasingly becoming limiting factors. Moreover, there is an inherent conflict in the design between power density and efficiency of an electric motor. Thus, a main focus in today's development lies on space-saving and yet effective and innovative cooling systems. This paper presents an approach for a multi-physical optimization that combines the domains of electromagnetics and thermodynamics. Based on a reference machine, this simulative study examins a total of nine different stator cooling concepts varying the cooling duct positions and end-winding cooling concepts.

The automotive PowerNet is facing a major transformation. The three main drivers are: • Increasing power • Availability requirements • PowerNet complexity and cost reduction These driving factors result in a wide variety of possible future PowerNet topologies. The increasing power demand is among others caused by the progressive electrification of formerly mechanical components and the trend of increasing number of comfort loads. This leads to a steady increase in installed electrical power. X-by-wire systems and autonomous driving functions result in higher availability requirements. As a result, the power supply of all safety-critical loads must always be kept sufficiently stable. To reduce costs and increase reliability, the car manufacturers aim to reduce the complexity of the PowerNet System, including the wiring harness and the controller network. The wiring harness e.g., is currently one of the costliest parts of modern cars. These challenges are met with different concepts.

Autonomous Emergency Braking (AEB) systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. Advancements in sensor technology and deep learning have improved vehicle perception and real-world understanding. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

As the market for electric vehicles grows, so does the demand for appropriate charging infrastructure. The availability of sufficient charging points is essential to increase public acceptance of electric vehicles and to avoid the so-called “charging anxiety”. However, the charging stations currently installed may not be able to meet the full charging demand, especially in areas where there is a general lack of grid infrastructure, or where the fluctuating nature of charging demand requires flexible, high-power charging solutions that do not require expensive grid extensions. In such cases, the use of mobile charging stations provides a good opportunity to complement the existing charging network. This paper presents a prototype of a mobile charging solution that is being developed as part of an ongoing research project, and discusses different use cases.

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

EU legislation provides for only local CO2 emission-free vehicles to be allowed in individual passenger transport by 2035. In addition, the directive provides for fuels from renewable sources, i.e. defossilised fuels. This development leads to three possible energy sources or forms of energy for use in individual transport. The first possibility is charging with electricity generated from renewable sources, the second possibility is hydrogen generated from renewable sources or blue production path. The third possibility is the use of renewable fuels, also called e-fuels. These fuels are produced from atmospheric CO2 and renewable hydrogen. Possible processes for this are, for example, methanol or Fischer-Tropsch synthesis. The production of these fuels is very energy-intensive and large amounts of renewable electricity are needed.

Previous studies have shown that dosing AdBlue into the exhaust system of diesel engines to reduce nitrogen oxides can lead to an increase in the number of particles (PN). In addition to the influencing factors of exhaust gas temperature, exhaust gas mass flow and dosing quantity, the dosed medium itself (AdBlue) is not considered as a possible influence due to its regulation in ISO standard 22241. However, as the standard specifies limit value ranges for the individual regulated properties and components for newly sold AdBlue, in reality there is still some margin in the composition. This paper investigates the particle number increase due to AdBlue dosing using several CPCs. The increase in PN is determined by measuring the number of particles after DPF and thus directly before dosing as well as tailpipe. Several AdBlue products from different sources and countries are measured and their composition is also analyzed with regard to the limit values regulated in the standard.

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.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.

Expansion chamber mufflers are commonly applied to reduce noise in HVAC. Dissipative materials, such as microperforated plates (MPPs), are often applied to achieve a more broadband mitigation effect. Such mufflers are typically characterized in the frequency domain, assuming time-harmonic excitation. From a computational point of view, transient analyses are more challenging. A transformation of the equivalent fluid model or impedance boundary conditions into the time domain induces convolution integrals. We apply the recently proposed finite element formulation of a time domain equivalent fluid (TDEF) model to simulate the transient response of dissipative acoustic media to arbitrary unsteady excitation. As most time domain approaches, the formulation relies on approximating the frequency-dependent equivalent fluid parameters by a sum of rational functions composed of real-valued or complex-conjugated poles.

Authors : Thomas ANTOINE, Christophe THEVENARD, Pierrick BOTTA, Jerome DESTREE, Alain Le Quenven Future noise emission limits for passenger car are going to lower levels by 2024 (Third phase of R51-03, with a limit of 68dBA for the pass by noise) –Social cost of noise for France in 2021, shows clearly that the dominant source of noise pollution is indeed road traffic (81 Bn€ for a total of 146 Bn€) This R51 regulation is meant to lower the noise pollution from road traffic, however when looking closer to the sound source and their contributions, in particular the tire/road noise interaction, the environmental efficiency of this regulation is questionable. Indeed: Tire/Road interaction involves tires characteristics, that are constrained by an array of specification for energy efficiency, safety (wet grip, braking, etc…) and it has been proven that there is a physical limit to what could be expected from the tire as far as tire/road interaction noise is concerned.

This study explores the feasibility of using a sustainable lignin-based fuel, consisting of 44 % lignin, 50 % ethanol, and 6 % water, in conventional compression ignition (CI) marine engines. Through experimental evaluations on a modified small-bore CI engine, we identified the primary challenges associated with lignin-based fuel, including engine startup and shutdown issues due to solvent evaporation and lignin solidification inside the fuel system, and deposit formation on cylinder walls leading to piston ring seizure. To address these issues, we developed a fuel switching system transitioning from lignin-based fuel to cleaning fuel with 85 vol% of acetone, 10 vol% of water and 5 vol% of ignition improving additive, effectively preventing system clogs.