@inproceedings{cb5e8b8d839d4218b68421630c587076,
title = "Electric Vehicle Thermal Management System Modeling with Informed Neural Networks",
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 take place within the EV, and Feature Generation via Loss Maps where loss maps of the inverter and the electric engine are used to generate a new power loss feature. Results show that accuracy increased by 1.7% to 3.6% depending on applied features and the neural network architecture.",
keywords = "Deep Neural Network, Electric Vehicle (EV), Machine learning, System modeling, Thermal Management",
author = "Bicer, {Ekin Alp} and Schirmer, {Pascal A.} and Peter Schreivogel and Gabriele Schrag",
note = "Publisher Copyright: {\textcopyright} 2023 EPE Association.; 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe ; Conference date: 04-09-2023 Through 08-09-2023",
year = "2023",
doi = "10.23919/EPE23ECCEEurope58414.2023.10264482",
language = "English",
series = "2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe",
}