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Title:

Electric Vehicle Thermal Management System Modeling with Informed Neural Networks

Document type:
Konferenzbeitrag
Author(s):
Bicer, E. A.; Schirmer, P. A.; Schreivogel, P.; Schrag, G.
Pages contribution:
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...     »
Keywords:
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 Decimal Classification:
620 Ingenieurwissenschaften
Book / Congress title:
2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe)
Date of congress:
04.-08.09.2023
Publisher:
IEEE
Date of publication:
04.09.2023
Year:
2023
Quarter:
3. Quartal
Year / month:
2023-09
Language:
en
Fulltext / DOI:
doi:10.23919/epe23ecceeurope58414.2023.10264482
WWW:
https://ieeexplore.ieee.org/document/10264482
TUM Institution:
Electrical Engineering; Professorship of Microsensors and Actuators
Ingested:
08.10.2024
Last change:
08.10.2024
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