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Group by: No Grouping | Item Type | Date | Language
Number of items: 11.

Eichenlaub, Tobias ; Heckelmann, Paul ; Rinderknecht, Stephan (2023):
Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds.
In: Vehicles, 5 (1), pp. 1-23. MDPI, ISSN 2624-8921,
DOI: 10.3390/vehicles5010001,

Liu, Zhihong ; Eichenlaub, Tobias ; Rinderknecht, Stephan (2023):
A survey of sequential adaptive sampling strategy for transmission power loss measurement.
In: Mechanical Systems and Signal Processing, 183, Elsevier, e-ISSN 0140-6736,
DOI: 10.1016/j.ymssp.2022.109644,

Eichenlaub, Tobias ; Rinderknecht, Stephan (2021):
Anticipatory Longitudinal Vehicle Control using a LSTM Prediction Model.
In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 19-22 Sept. 2021, Indianapolis, IN, USA, pp. 447-452,
New York, IEEE, ISBN 978-1-7281-9142-3,
DOI: 10.1109/ITSC48978.2021.9564787,
[Conference or Workshop Item]

Leise, Philipp ; Eßer, Arved ; Eichenlaub, Tobias ; Schleiffer, J.-E. ; Altherr, Lena C. ; Rinderknecht, Stephan ; Pelz, P. F. (2021):
Sustainable system design of electric powertrains - comparison of optimization methods.
In: Engineering Optimization, Taylor & Francis, ISSN 0305-215X,
DOI: 10.1080/0305215X.2021.1928660,

Eichenlaub, Tobias ; Rinderknecht, Stephan (2021):
Intelligent Set Speed Estimation for Vehicle Longitudinal Control with Deep Reinforcement Learning.
pp. 101-107, Darmstadt, Association for Computing Machinery, 2021 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE 2021), Seoul, Republic of Korea, 19.02.2021, ISBN 978-1-4503-8983-9,
DOI: 10.1145/3459104.3459123,
[Conference or Workshop Item]

Eßer, Arved ; Eichenlaub, Tobias ; Schleiffer, J.-E. ; Jardin, Philippe ; Rinderknecht, Stephan (2020):
Comparative evaluation of powertrain concepts through an eco-impact optimization framework with real driving data.
In: Optimization and Engineering, 21 (3), Springer, ISSN 13894420, e-ISSN 15732924,
DOI: 10.1007/s11081-020-09539-2,

Eßer, Arved ; Schleiffer, J.-E. ; Eichenlaub, Tobias ; Rinderknecht, Stephan (2020):
Development of an Optimization Framework for the Comparative Evaluation of the Ecoimpact of Powertrain Concepts.
In: VDI-Berichte, 2354, Bonn, 10.-11. Juli 2019, 19. Internationaler VDI-Kongress "Dritev - Getriebe in Fahrzeugen", ISBN 978-3-18-092354-3,
DOI: 10.25534/tuprints-00011299,
[Conference or Workshop Item]

Eßer, Arved ; Eichenlaub, Tobias ; Rinderknecht, Stephan (2020):
Real-Driving-Based Comparison of the Eco-Impact of Powertrain Concepts using a Data-Driven Optimization Environment.
In: VDI-Berichte, 2373, pp. 309-342, Onlinekonferenz, 20. Internationaler VDI-Kongress "Dritev - Getriebe in Fahrzeugen", 24-25 Juni, ISSN 0083-5560, ISBN 978-3-18-092373-4,
DOI: 10.25534/tuprints-00011933,
[Conference or Workshop Item]

Jardin, Philippe ; Eßer, Arved ; Givone, S. ; Eichenlaub, Tobias ; Schleiffer, J.-E. ; Rinderknecht, Stephan (2019):
The Sensitivity in Consumption of Different Vehicle Drivetrain Concepts Under Varying Operating Conditions: A Simulative Data Driven Approach.
In: Vehicles — Open Access Journal from MDPI, [Article]

Rinderknecht, Stephan ; Eßer, Arved ; Schleiffer, J.-E. ; Eichenlaub, Tobias (2018):
Comparative Real-Driving Optimization of Drivetrain Concepts regarding the Ecological Impact - A Big Data Approach for the Fleet.
eDSIM Conference 2018 - Electrified Drivelines & Engineering 4.0, Darmstadt, Deutschland, [Conference or Workshop Item]

Eßer, Arved ; Schleiffer, J.-E. ; Eichenlaub, Tobias ; Kuznik, Alexander ; Seier, Maximilian ; Weyand, Steffi ; Nordenholz, Falko ; Steck, Felix ; Kröger, Lars ; Schebek, Liselotte ; Beidl, Christian ; Rinderknecht, Stephan (2018):
Verbrauchs- und Emissionsbewertung von Fahrzeugantriebskonzepten für die Langstreckenmobilität der Zukunft : Abschlussbericht FahrKLang : Berichtszeitraum: 01.10.2017-30.09.2018.
Darmstadt, Technische Universität Darmstadt, DOI: 10.2314/KXP:1696925533,

This list was generated on Tue Feb 7 01:35:38 2023 CET.