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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.

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.

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 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.

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.