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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.

With the law on autonomous driving and the associated ordinance, it has been possible in Germany since June 2022 to grant operating permission to vehicles with automated driving function and to admit them to road traffic. For public transport, automated shuttle buses offer the opportunity to maintain the existing service despite a shortage of personnel and to close gaps in supply. The safety of vehicle occupants, other road users and third parties is a key concern in the operation of automated shuttle buses. As part of the project RABus “Real laboratory for automated bus operation in public transport in urban and rural areas”, which is funded by the Ministry of Transport Baden-Württemberg, the operational reliability of automated shuttle buses (SAE Level 4) in public transport is being scientifically investigated. In the implementation of automated driving, obstacle detection/avoidance is a safety-critical driving function.

In this work, a novel approach is introduced comprising a combination of unsupervised machine learning (ML) scheme and charging energy management of electric vehicles (EV). The main goal of this implementation is to reduce the load peak of charging EV’s, which are regular users of electric vehicle supply equipment (EVSE) of a certain building and, at the same time, to meet their electric and behavioral demands. The unsupervised ML considers certain features within the charging profiles in addition to the behavioral characteristics of the EV based on its intended use. Moreover, these features are extracted from large sets of history measurement data of the EVSE, which are stored in the data bank. The ML categorizes the EVs within certain clusters having defined specifications.

Electric Vehicles are subject to effects that lead to more or less rapid degradation of functions. This can cause hazards for the drivers and uninvolved road participants. For this reason, the must be detected and mitigated, to maintain the vehicle function even in critical situations until a safe operating mode can be established. This publication presents an intelligent digital twin, located in the edge and connected with an electric vehicle via 5G. That can improve the operation of electrified vehicles by enabling the online detection of abnormal situations in the electrified powertrain and vehicle dynamics. Its core component is the fault detection system, which is implemented based on a 1-Nearest Neighbor algorithm. It is initially trained on synthetic data, generated in CarMaker for real-world powertrain issues such as demagnetization and open-/short-switch failures, using detailed mathematical models.

Within the last years, the number of electric vehicles in Europe is increasing faster than the number of charging stations. Based on this, the reliability of the charging process takes on greater significance. The communication between the electric vehicle (EV) and their charging station is a prerequisite to transfer the charging power. Using a universal charging communication test system allows an observation of the whole charging communication according to ISO 15118 and IEC 61851 standards. It is important to know whether the communication between the EV and the charging station functions accordingly, in order to ensure functionality. Furthermore, tolerances must comply with the standard. In a first step, the basic communication according to IEC 61851 is considered. Preliminary investigations of the pulse width modulation (PWM) signal have shown that some EVs are tolerant of the standards.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

The adequate dimensioning of drive train components such as gearbox, clutch and driveshaft presents a major technical task. The one of manual transmissions represents a special significance due to the customer’s ability of inducing high force, torque and thermic energy into the powertrain through direct mechanical interconnection of gearstick, clutch pedal and gearbox. Out of this, the question about how to capture behavior and strain of the components during real operation, as well as their objective evaluation evolves. Furthermore, the gained insights must be considered for designing and development. As a basis for the examination, measuring data from imposing driving tests are adduced. Therefore, a trial study has been conducted, using a representative circular course in the metropolitan area of Stuttgart, showing the average German car traffic. The more than 40 chosen drivers constitute the average driver in Germany with respect to age, gender and annual mileage.

According to ISO 26262, ASIL decomposition is the breakdown of a top level safety requirement derived from safety goal into redundant safety requirements with sufficient independence to achieve the safety goal independently. The usage of decomposition enables the opportunity to reduce the ASIL rating of the decomposed safety requirements of a safety goal. To apply decomposition, the decomposed safety requirements should be allocated to sufficiently independent architectural elements. If the redundant/decomposed safety requirements cannot be allocated to sufficiently independent architectural elements, then these redundant safety requirements inherit the initial ASIL of the safety goal. ASIL decomposition can be applied to the functional, technical, hardware or software safety requirements of the item.

In recent years the automotive companies are developing their self-driving technology very rapidly. Most of them want to launch their self-driving vehicles with SAE level 4 at the beginning of 2020. The main goal of the development of self-driving cars is to reduce accidents caused by driver errors. But there are some technological challenges to solve such as increasing of the safety and availability in order to get the acceptance from the customers. The purpose of this research is to investigate the possible fail-operational safety architectures for both conventional systems as powertrain and the entire ADAS processing chain.

Safety is becoming more and more important with the ever increasing level of safety related E/E Systems built into the cars. Increasing functionality of vehicle systems through electrification of power train and autonomous driving leads to complexity in designing system, hardware, software and safety architecture. The application of multicore processors in the automotive industry is becoming necessary because of the needs for more processing power, more memory and higher safety requirements. Therefore it is necessary to investigate the safety solutions particularly for Automotive Safety Integrity Level (ASIL-D) Systems. This brings additional challenges because of additional requirements of ISO 26262 for ASIL-D safety concepts. This paper presents an approach for model-based “dependent failure analysis” which is required from ISO 26262 for ASIL-D safety concepts with decomposition approach.

This paper proposes a framework for semi-autonomous longitudinal guidance for electric vehicles. To lower the risk for pedestrian collisions in urban areas, a velocity trajectory which is given by the driver is optimized with respect to safety aspects with the help of Nonlinear Model Predictive Control (NMPC). Safety aspects, such as speed limits and pedestrians on the roadway, are considered as velocity and spatial constraints within prediction horizon in NMPC formulation. A slack variable is introduced to enable overshooting of velocity constraints in situations with low risk potential to rise driver acceptance. By changing the weight of slack variable, the control authority can be shifted continuously from driver to automation. Within this work, a prototypical real-time implementation of the longitudinal guidance system is presented and the potential of the approach is demonstrated in human-in-the-loop test drives in the Stuttgart Driving Simulator.

As part of a system model, a PEM fuel cell stack model is presented for functional tests and pre-calibration of control units on hardware-in-the-loop (HiL) test benches. From the basic idea to couple a 1-D membrane model with a spatially distributed abstraction of the gas channel, a real-time capable 1-D+1-D PEM FC stack model is constructed. Fundament for the HiL usage is an explicit formulation of the commonly implicit model equations. With that, not only calculation time can be reduced, but also model accuracy is preserved. A validation using test bench data emphasizes the accuracy of the model. Finally, a runtime and eigenvalue analysis of the stack model proves the real-time capability.

This paper addresses the performance and potential of using electric vehicles in the Gulf Arab states. Based on a survey executed in Salalah, Oman, a representative test driving cycle has been set up. This cycle is the first of its kind for this region, where it is driven with a vehicle provided with special measurement equipment to log important values, e.g. vehicle's speed and position, temperatures and solar irradiance. More than 40 test drives are performed to obtain a representative driver profile. The driving cycle and driver profile are used in a simulation model which is capable of simulating the energy consumption for internal combustion engine or electric motor propulsion systems. The simulation model which contains detailed models for the driver, driving cycle, vehicle components and its dynamics is validated and used to compare the consumed energy for the two different propulsion systems.

This paper presents an autonomous cruise control for battery electric vehicles. The presented approach is based on the usage of predictive route data which is extracted out of a digital map and a wide range radar system in order to capture vehicles in front. By using the predictive route data and the information of the radar system, the autonomous cruise control can control the vehicle's speed over a wide range of driving situations without any driver interaction. The main aim of the presented autonomous cruise control is to optimize the battery electric vehicle's energy consumption. The main idea is to use predictive route data in order to calculate a consumption optimal vehicle speed trajectory by means of online optimization. The benefits of the autonomous cruise control are shown by means of real test drives and measured data evaluation.

This report presents a new compensation method for distortions related to dead time, caused by B6-inverters with pulse-width-modulated output voltages. In spite of low sampling rates, the new method of compensation is effective at all ranges of rotation speed up to the field weakening region. No additional hardware is required for its implementation. The effectiveness of the new method has been shown experimentally. A description of the relevant distortions is given first to provide a basis for the development. This considers the field weakening region, and offers an illustrative method of quantifying the distortions. It is also shown that the use of compensation methods that do not take the sampling time into account leads to additional distortions. It is even possible that they exceed the distortions in an equivalent system without compensation.

An approach for model-based control strategy design for diesel hybrid drive-trains has been developed, permitting the reduction of fuel consumption as well as of exhaust gas emissions. The control strategy consists of four core-functions: the SOC-management, the operation mode determination, the gear selection, and the thermal monitoring. Based on those different interpretations, a control strategy can be designed that leads to great reductions in fuel consumption or alternatively to a mentionable decline of nitrous oxides. In this trade-off, both aims can not be optimized at a time. Though, the strategy to be used is a compromise, designs for control strategies are possible that reduce both for a significant amount. Extending this control strategy by adding functions for transient behavior at start-up and load changes; phlegmatization enables additional potentials for emission reduction.

In this paper it will be shown how a database containing information of the road characteristics of a frequently driven route can be automatically generated and continually updated in a vehicle during each drive. The contained information can be used as foresight information in predictive driving strategies. By using only drive train information, standard sensors (e.g. from ESC and ABS), and a GPS relevant road characteristics (curves, slopes, speed limits, etc) can be identified during the drive, stored in an on-board database, and used to optimize fuel consumption or driving comfort in subsequent trips along the route. The system is verified using a driving simulator with a 3D surround graphics system.

Electronic control units of engines have an ever-increasing complexity and more and more software functions need to get calibrated. Today it is possible to automate virtually every test procedure needed for these calibration tasks. But with currently available systems the effort required to set up automatic procedures often outweighs the advantages even if a commercial toolbox is readily available. The goal of this paper is to show a new concept for a calibration system that allows for creation and modification of test sequences with relatively little knowledge and reuse of test procedure components. Included is a language that features special commands and integrated knowledge for engine test bed applications. Flexible handling of exception situations, reusability of test procedure components and extensibility of the language will be discussed. The implementation of a typical test procedure will be shown.

The development of modern engine management systems makes ever-more stringent demands of the tools used. In future, the Hardware-in-the-Loop (HiL) simulation, used primarily for hardware and software tests to date, is also to be used for control function parameter adaptation tasks. This results in the need to provide highly precise, real-time-capable simulation models in all phases of the development process. This can be done by the use of modern methods for identification of non-linear, static and dynamic multi-variable systems, partly in conjunction with conventional physical model structures. In particular, artificial neural networks prove flexible in use in this case. This allows modelling dependent on the information available in the various phases of the engine development process. Thus, in the early phase, it is possible to develop engine models with computation results from complex engine simulation programs such as PROMO or GT Power.

Multiple injection is potentially capable of reducing emissions of common-rail diesel engines at part load. The slope of the needle lift of today’s hydraulically operated common-rail injectors is very weak. For that reason, the fuel flow is throttled in that phase and the mixture formation suffers. The presented injector, which is directly actuated by a piezo, shows the same very steep needle lift as a unit injector. This results in a better mixture formation. A needle lift control allows to open orifices at different levels and to adjust homogenisation. The hardware in the loop simulation shows the exact needle lift control and its influence on the mixture formation.