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In this paper we are presenting the concept of automated park and charge functions in different use scenarios. The main scenario is automated park and charge in production and the other use scenario is within automated valet parking in parking garages. The automated park and charge in production is developed within the scope of the publicly funded project E-Self. The central aim of the project is the development and integration of automated driving at the end-of-line in the production at Ford Motor Company's manufacturing plant in Cologne. The driving function thereby is mostly based upon automated valet driving with an infrastructure based perception and action planning. Especially for electric vehicles the state of charge of the battery is critical, since energy is needed for all testing and driving operations at end-of-line.

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.

When using "green" hydrogen, fuel cell technology plays a key role in emission-free mobility. A powertrain based on fuel cells (FC) shows its advantages over battery-electric powertrains when the requirement profile primarily demands high performance over a longer period of time, high flexible availability and short refueling times. In addition, FC achieves higher effi-ciencies than the combustion of hydrogen in a gas engine, meaning that the chemical energy is used more efficiently than with established combustion engines. When using FC technology, numerous companies in Baden-Württemberg can contribute their specific expertise from the traditional automotive construction and supplier business. This includes auxiliary units in the air (cathode) and hydrogen (anode) path, such as the air com-pressor, the H2 recycling pump, humidifier, cooling system, power electronics, valve and pressure tank technology as well as components of the fuel cell stack itself.

To shape future mobility MAHLE has committed itself to foster wireless charging for electrical vehicles. The standardized wireless power transfer of 11 kW at a voltage level of 800 V significantly improves the end user experience for charging an electric vehicle without the need to handle a connector and cable anymore. Combined with automated parking and autonomous driving systems, the challenge to charge fleets without user interaction is solved. Wireless charging is based on inductive power transfer. In the ground assembly’s (GA) power transfer coil, a magnetic field is generated which induces a voltage in the vehicle assembly (VA) power transfer coil. To transfer the power from grid to battery with a high efficiency up to 92% the power transfer coils are compensated with resonant circuits. In this paper the Differential-Inductive-Positioning-System (DIPS) to align a vehicle on the GA for parking will be presented.

In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm based on GNSS, vehicle odometry, IMU and LiDAR data. The algorithm is adapted to the use-case by adding a velocity factor and altitude data from a Digital Terrain model. Based on the map a state estimator is proposed, which combines high-frequency LiDAR odometry based on FAST-LIO with low-frequency absolute multiscale ICP-based LiDAR position estimation.

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.

Due to its physical and chemical properties, hydrogen is an attractive fuel for internal combustion engines, providing grounds for studies on hydrogen engines. It is common practice to use a mathematical model for basic engine design and an essential part of this is the simulation of the combustion cycle, which is the subject of the work presented here. One of the most widely used models for describing combustion in gasoline and diesel engines is the Wiebe model. However, for cases of hydrogen combustion in DI engines, which are characterized by mixture stratification and in some cases significant incomplete combustion, practically no data can be found in the literature on the application of the Wiebe model. Based on Wiebe's formulas, a mathematical model of hydrogen combustion has been developed. The model allows making computations for both DI and PFI hydrogen engines. The parameters of the Wiebe model were assessed for three different engines in a total of 26 operating modes.

Increasing urbanisation and the growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry standard load cases for the strength design of the components. To design the chassis components, first the available installation space is determined and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify component geometries that are suitable for the force flow. The optimisation setup is selected in a way that allows to integrate information, energy and material-carrying conductors into the suspension arms. The conductors even serve as load-bearing structures because of the matching design of the components.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

Engine and aftertreatment solutions have been identified to meet the upcoming ultra-low NOX regulations on heavy duty vehicles in the United States and Europe. These standards will require changes to current conventional aftertreatment systems for dealing with low exhaust temperature scenarios while increasing the useful life of the engine and aftertreatment system. Previous studies have shown feasibility of meeting the US EPA and California Air Resource Board (CARB) requirements. This work includes a 15L diesel engine equipped with cylinder deactivation (CDA) and an aftertreatment system that was fully DAAAC aged to 800,000 miles. The aftertreatment system includes an e-heater (electric heater), light-off Selective Catalytic Reduction (LO-SCR) followed by a primary aftertreatment system containing a DPF and SCR.

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.