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ATZelectronics worldwide
Published in: ATZelectronics worldwide 12/2023

01-12-2023 | Research

Prediction-based Optimization of Electric Powertrain Operation Using Silent Testing of Automated Driving Functions

Authors: Melina Lutwitzi, Steven Peters, Ivan Cvok

Published in: ATZelectronics worldwide | Issue 12/2023

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Excerpt

Operating strategies for complex electric powertrains with multiple electric machines are often based on highly simplified control models to enable sufficiently fast strategy optimizations. In the following, TU Darmstadt and Rimac present an approach that uses environment perception to predict future driving behavior and thus gain additional time for the use of more accurate optimization models. In this way, perception and planning modules currently under development for automated driving functions can be used in a non-safety related function. This enables a new type of so-called silent testing and additionally creates a perceptible added value for the vehicle user. …
previous article Data Quality as Key to Driver Assistance System Development
next article Containerization for the Software-defined Vehicle

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Metadata
Title
Prediction-based Optimization of Electric Powertrain Operation Using Silent Testing of Automated Driving Functions
Authors
Melina Lutwitzi
Steven Peters
Ivan Cvok
Publication date
01-12-2023
Publisher
Springer Fachmedien Wiesbaden
Published in
ATZelectronics worldwide / Issue 12/2023
Electronic ISSN: 2524-8804
DOI
https://doi.org/10.1007/s38314-023-1554-5

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