Search Results

The paper focuses on an optimization methodology, which uses Design of Experiments (DoE) methods to define component parameters of parallel hybrid powertrains such as number of gears, transmission spread, gear ratios, progression factor, electric motor power, electric motor nominal speed, battery voltage and cell capacity. Target is to find the optimal configuration based on specific customer targets (e.g. fuel consumption, performance targets). In the method developed here, the hybrid drive train configuration and the combustion engine are considered as fixed components. The introduced methodology is able to reduce development time and to increase output quality of the early system definition phase. The output parameters are used as a first hint for subsequently performed detailed component development. The methodology integrates existing software tools like AVL CRUISE [5] and AVL CAMEO [1].

A steady increasing share and complexity of automotive software is a huge challenge for quality management during software development and in-use phases. In cases of faults occurring in customer’s use, warranty leads to product recalls which are typically associated with high costs. To avoid software faults efficiently, quality management and enhanced development processes have to be realized by the introduction of specific analysis methods and Key Process/Performance Indicators (KPIs) to enable objective quality evaluations as soon as possible during product development process. The paper introduces an application of specific analysis methods by using KPIs and discusses their potential for automotive software quality improvement. Target is to support quality evaluation and risk-analysis for the release process of automotive software.

Hybrid electric vehicles (HEVs) with a power-split system offer a variety of possibilities in reduction of CO2 emissions and fuel consumption. Power-split systems use a planetary gear sets to create a strong mechanical coupling between the internal combustion engine, the generator and the electric motor. This concept offers rather low oscillations and therefore passive damping components are not needed. Nevertheless, during acceleration or because of external disturbances, oscillations which are mostly influenced by the ICE, can still occur which leads to a drivability and performance downgrade. This paper proposes a design of an active damping control system which uses the electric motor to suppress those oscillations instead of handling them within the ICE control unit. The control algorithm is implemented as part of an existing hybrid controller without any additional hardware introduced.

Virtual sensing, i.e., the method of estimating quantities of interest indirectly via measurements of other quantities, has received a lot of attention in various fields: Virtual sensors have successfully been deployed in intelligent building systems, the process industry, water quality control, and combustion process monitoring. In most of these scenarios, measuring the quantities of interest is either impossible or difficult, or requires extensive modifications of the equipment under consideration – which in turn is associated with additional costs. At the same time, comprehensive data about equipment operation is collected by ever increasing deployment of inexpensive sensors that measure easily accessible quantities. Using this data to infer values of quantities which themselves are impossible to measure – i.e., virtual sensing – enables monitoring and control applications that would not be possible otherwise.