TY  - THES
AB  - The work presented here should fulfil the requirements for the granting of the degree of
Doctor of Engineering at the University Siegen. It was completed within the EU funded
project eFuture with the company Intedis. The goal of the project was to create an
efficient and safe electric vehicle on the basis of a Tata eVista with help of a complete
new architecture.
A novel robust vehicle observer was designed for an optimal support of the integrated
driver assistance systems. The concept for the observer is based upon an extended
Kalman Filter using a non-linear vehicle model and the Dugoff tire model.
Moreover, a parameter estimation and a plausibility check of the sensor signals were
developed to increase the robustness of the observer. The estimation of the vehicle
mass, the effective tire radii and the road adhesion were designed with an event-seeking
characteristic in order to minimise the computational load. In the plausibility check
delayed or faulty sensor signals are detected and corrected. Here the newly designed
replacement of delayed or missing sensor signals by the concept of Markov Chains is
pointed out. By this, the correctness of the output signals and the safety of the vehicle
can be guaranteed for a defined time. Additionally, the evaluation of the stability limits
and the driven distance of the vehicle are computed under the use of quantities that
were calculated before. After the model based design the software was integrated on the
hardware of the prototype. The functionality of this concept is given by results during
dynamic test drives
AU  - Korte, Matthias
DA  - 2016
KW  - Fahrerassistenzsystem
KW  - Vehicle State Observation
KW  - Kalman Filtering
KW  - Parameter Estimation
KW  - Vehicle Dynamics
KW  - Vehicle Model
LA  - eng
PY  - 2016
TI  - Robust vehicle state and parameter observation : adaptive filtering concept with enhanced robustness by usage of Markov chains
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:467-10802
Y2  - 2024-11-22T04:41:04
ER  -