Benutzer: Gast  Login
Titel:

Electric Vehicle Thermal Management System Modeling with Informed Neural Networks

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Bicer, E. A.; Schirmer, P. A.; Schreivogel, P.; Schrag, G.
Seitenangaben Beitrag:
1-8
Abstract:
Proper modeling of Thermal Management System (TMS) in Electric Vehicles (EVs) is crucial in terms of designing the EV components. Data-driven methods come up as an alternative to the computationally intensive high-fidelity methods or reduced order models where the accuracy is sacrificed for performance. In this paper, two informed neural network approaches are benchmarked in EV TMS modeling: Analytical Feature Engineering, where new features are generated by using the physical processes that tak...     »
Stichworte:
Bicer; Schrag; Schreivogel; Schirmer; TMS; Thermal; EV; Hi-Fi; Time-frequency analysis; Thermal engineering; Computer architecture; Benchmark testing; Coolants; Thermal management; Electric vehicles; Thermal Management; Machine learning; System modeling; Deep Neural Network; Electric Vehicle (EV)
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe)
Datum der Konferenz:
04.-08.09.2023
Verlag / Institution:
IEEE
Publikationsdatum:
04.09.2023
Jahr:
2023
Quartal:
3. Quartal
Jahr / Monat:
2023-09
Sprache:
en
Volltext / DOI:
doi:10.23919/epe23ecceeurope58414.2023.10264482
WWW:
https://ieeexplore.ieee.org/document/10264482
TUM Einrichtung:
Electrical Engineering; Professorship of Microsensors and Actuators
Eingabe:
08.10.2024
Letzte Änderung:
08.10.2024
 BibTeX