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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 courier express parcel service industry (CEP industry) has experienced significant changes in the recent years due to increasing parcel volume. At the same time, the electrification of the vehicle fleets poses additional challenges. A major advantage of battery electric CEP vehicles compared to internal combustion engine vehicles is the ability to regenerate the kinetic energy of the vehicle in the frequent deceleration phases during parcel delivery. If the battery is cold the maximum recuperation power of the powertrain is limited by a reduced chemical reaction rate inside the battery. In general, the maximum charging power of the battery depends on the state of charge and the battery temperature. Due to the low power demand for driving during CEP operation, the battery self-heating is comparably low under cold ambient conditions. Without active conditioning of the battery, potential regenerative energy is lost as a result of the cold battery.

In vehicle NVH development, vibroacoustic simulations with Finite Element (FE) models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time, the need for multiple evaluations of the same model with different input parameters, e.g., for uncertainty quantification, optimization, or robustness investigations, is also increasing. Therefore, it is crucial to reduce the computational costs in these cases. A common technique is to use surrogate models that replace the computationally intensive FE model to perform repeated evaluations. Several different methods in this area are well established, but with the continuous advancements in the field of machine learning, interesting new methods like the Gaussian Process (GP) regression arises as a promising approach.

The development of highly automated driving functions (AD) recently rises the demand for so called Fail-Operational systems for native driving functions like steering and braking of vehicles. Fail-Operational systems shall guarantee the availability of driving functions even in presence of failures. This can also mean a degradation of system performance or limiting a system’s remaining operating period. In either case, the goal is independency from a human driver as a permanently situation-aware safety fallback solution to provide a certain level of autonomy. In parallel, the connectivity of modern vehicles is increasing rapidly and especially in vehicles with highly automated functions, there is a high demand for connected functions, Infotainment (web conference, Internet, Shopping) and Entertainment (Streaming, Gaming) to entertain the passengers, who should no longer occupied with driving tasks.

The technology in the automotive industry is evolving rapidly in recent times. Thus, with the development of new technologies, the challenges are also ever-increasing from an Electromagnetic Interference and Susceptibility (EMI/EMC) perspective. A lot of the latest technologies in Adaptive Driver Assistance Systems (ADAS), which include Rear Drive Assist, Blind Spot Detection (BSD), Lane Change Assist (LCA) to name a few, and other features like Anti-Braking System (ABS), Emergency Brake Assist (EBD) etc. rely heavily on different types of sensors and their detection circuitry. In addition, a lot of other internal functions in the Engine Control Unit (ECU) also depend on such sensors’ functionalities. Thus, it becomes imperative to study the potential impact of higher field emissions on the immunity behaviour of the sensors.

The automotive world is progressing fast towards autonomous vehicles making sensors one of the critical components. There is a requirement for constant exchange of information between the vehicle and its surrounding environment, which is assisted by sensors such as Camera, LiDAR, and RADAR. However, exposure to harsh environmental conditions such as rain, dirt, snow, and bird droppings can hamper the functioning of the sensors and in turn interrupt accurate vehicle maneuvers. Sensor-cleaning mechanisms are required to be tested under various weather conditions and vehicle operating situations. Besides wind tunnel tests, digitalizing this whole process becomes important to take decision on design changes in early vehicle development stage. This work presents a digital methodology to test the LiDAR cleaning system in the advent of mud clearing at different vehicle speeds. The cleaning mechanism consists of a telescopic nozzle placed above the LiDAR translating back and forth.

The rapidly growing amount of software in cars reshapes the automotive industry. The software has a significant influence on the production lines, due to the time required to flash it onto the vehicle and its capabilities to test vehicle functions during production. In this paper we identify the main pain points regarding software in the manufacturing process by performing a structured analysis on the experiences made at a major car manufacturer over last two years. Consequently, the paper analyses the possible approaches to address the challenges.

More and more applications (apps) are entering vehicles. Customers would like to have in-car apps in their infotainment system, which they already use regularly on their smartphones. Other apps with new functionalities also inspire vehicle customers, but only as long as the customer can utilize them. To ensure customer satisfaction, it is important that these apps work and that failures are found and corrected as quickly as possible. Therefore, in-car apps also implicate requirements for future vehicle diagnostics. This is because current vehicle diagnostic methods are not designed for handling dynamic software failures of apps. Consequently, new diagnostic methods are needed to support the diagnosis of in-car apps. Log data are a central building block in software systems for system health management or troubleshooting. However, there are different types of log data and log environment setups depending on the underlying system or software platform.

The automotive industry changes rapidly. New players, concepts, and technologies from the Information Technology (IT) domain enter the market and software receives a high priority. Inside the vehicle, the number of components, which consist mostly of software, are increasing and more and more software-based functions are offered. In addition, High Performance Computers (HPCs) are continuing to be integrated into vehicles. These aspects lead to several challenges with current vehicle diagnostics, but also enable new opportunities in that field. However, in the specific area of vehicle diagnostics, there exists only very limited literature that considers current challenges and new possibilities for future vehicle diagnostics. Some literature deals with the general automotive system design or shows results from about five years ago. The viewpoints of an Original Equipment Manufacturer (OEM) are not included there.

Software complexity of vehicles is constantly growing especially with additional autonomous driving features being introduced. This increases the risk for bugs in the system, when the car is delivered. According to a car manufacturer, more than 90% of availability problems corresponding to Electronic Control Unit (ECU) functionality are either caused by software bugs or they can be resolved by applying software updates to overcome hardware issues. The main concern are sporadic errors which are not caught during the development phase since their trigger condition is too unlikely to occur or is not covered by the tests. For such systems, there is a need of safe and secure infield diagnosis. In this paper we present a tool software architecture with remote access, which facilitates standard read/write access, an efficient channel interface for communication and file I/O, and continuous trace.

The current automotive electronic and electrical (EE) architecture has reached a scalability limit and in order to adapt to the new and upcoming requirements, novel automotive EE architectures are currently being investigated to support: a) an Ethernet backbone, b) consolidation of hardware capabilities leading to a centralized architecture from an existing distributed architecture, c) optimization of wiring to reduce cost, and d) adaptation of service-oriented software architectures. These requirements lead to the development of Zonal EE architectures as a possible solution that require appropriate adaptation of used security mechanisms and the corresponding utilized hardware trust anchors. 1 The current architecture approaches (ECU internal and in-vehicle networking) are being pushed to their limits, simultaneously, the current embedded security solutions also seem to reveal their limitations due to an increase in connectivity.

The electric range of plug-in hybrids as well as the installed electric power has steadily increased. With an electric power share of more than half of the overall system power, concepts of hybrid electric vehicles with at least two electric machines come into focus. Especially the concept of adding an individual electric axle to a state-of-the-art parallel hybrid, such as a P2-hybrid, is promising. However, the system complexity of a so-called P24-hybird increases significantly because the number of possible system states rises. This leads to an increased development and calibration effort for an online energy management. Especially a transfer from an optimized operating strategy to a rule-based energy management is challenging. Thus, a development framework for the calibration of an online energy management system (EMS) which is as fuel efficient as possible is needed.

In order to adhere with future automotive legislation and incentives, the electric range of plug-in hybrids has steadily increased. At the same time, the installed electric power has risen as well leading to future hybrid vehicles with an electric power share of more than half of overall system power and hybrid configurations with at least two electrical machines come into focus. The concept of adding a separate electrical axle to a P2-hybrid - a so called P24-hybrid, is of special interest. The system complexity of a such a system increases significantly as the number of possible system states increases. Thus, this paper analyzes the efficiencies and benefits of the different system states within the fuel-optimal operating strategy derived by global optimization. By varying the electrical power distribution between the two axles, the impact on fuel efficiency and the changes within the operating strategy are investigated.

Sunroof buffeting is examined experimentally and numerically in this paper. Despite the fact that some consider the simulation process for sunroof buffeting to be mature, there remain substantial uncertainties even in recently published methodologies. Capturing the frequencies and especially the sound pressure levels correctly is essential if CFD simulations are intended to be used during early stages of a car development process. Numerous experimental results of sunroof buffeting and the interior low-frequency characteristics of a 2013 Mercedes-Benz S-Class have been used to develop a simplified car model: a full-size S-Class model with slightly simplified geometries in the interior as well as at the exterior. To avoid the effects of numerous different materials in the interior, it is solely made from polyurethane and aluminum and built to maximize its structural rigidity and air-tightness.

This paper explains why software for efficient model-based development is needed to improve the efficiency of automakers and suppliers when implementing solutions with next generation automotive embedded systems. The resulting synergies are an important contribution for the automotive industry to develop safer, smarter, and more eco-friendly cars. To achieve this, it requires implementations of algorithms for machine learning, deep learning and model predictive control within embedded environments. The algorithms’ performance requirements often exceed the capabilities of traditional embedded systems with a homogeneous multicore architecture and, therefore, additional computing resources are introduced. The resulting embedded systems with heterogeneous computing architectures enable a next level of safe and secure real-time performance for innovative use cases in automotive applications such as domain controllers, e-mobility, and advanced driver assistance systems (ADAS).

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.

The reduction of vehicle exhaust particle emissions is a success story of European legislation. Various particle number (PN) counters and calibration procedures serve as tools to enforce PN emission limits during vehicle type approval (VTA) or periodical technical inspection (PTI) of in-use vehicles. Although all devices and procedures apply to the same PN-metric, they were developed for different purposes, by different stakeholder groups and for different target costs and technical scopes. Furthermore, their calibration procedures were independently defined by different stakeholder communities. This frequently leads to comparability and interpretation issues. Systematic differences of stationary and mobile PN counters (PN-PEMS) are well-documented. New, low-cost PTI PN counters will aggravate this problem. Today, tools to directly compare different instruments are scarce.

This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

Recently, vehicle networks have increased complexity due to the demand for autonomous driving or connected devices. This increasing complexity requires high bandwidth. As a result, vehicle manufacturers have begun using Ethernet-based communication for high-speed links. In order to deal with the heterogeneity of such networks where legacy automotive buses have to coexist with high-speed Ethernet links vehicle manufacturers introduced a vehicle gateway system. The system uses Ethernet as a backbone between domain controllers and CAN buses for communication between internal controllers. As a central point in the vehicle, the gateway is constantly exchanging vehicle data in a heterogeneous communication environment between the existing CAN and Ethernet networks. In an in-vehicle network context where the communications are strictly time-constrained, it is necessary to measure the delay for such routing task.

The powertrain module is being introduced to embedded System on Chips (SoCs) designed to increase available computational power. These high-performance SoCs have the potential to enhance the computational power along with providing on-board resources to support unexpected feature growth and on-demand customer requirements. This project will investigate the radial basis function (RBF) using the Gaussian process (GP) regression algorithm, the ETAS ASCMO tool, and the hardware accelerator Advanced Modeling Unit (AMU) being introduced by Infineon AURIX 2nd Generation. ETAS ASCMO is one of the solutions for data-driven modeling and model-based calibration. It enables users to accurately model, analyze, and optimize the behavior of complex systems with few measurements and advanced algorithms. Both steady state and transient system behaviors can be captured.