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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.

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.

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.

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.

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.

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.

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.