---
_id: '65253'
article_number: '306'
author:
- first_name: Ali Hassan Ali
full_name: Abdelwanis, Ali Hassan Ali
last_name: Abdelwanis
- first_name: Barnabas
full_name: Haucke-Korber, Barnabas
id: '93461'
last_name: Haucke-Korber
orcid: 0000-0003-0862-2069
- first_name: Darius
full_name: Jakobeit, Darius
last_name: Jakobeit
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Marvin
full_name: Meyer, Marvin
last_name: Meyer
- first_name: Maximilian
full_name: Schenke, Maximilian
id: '52638'
last_name: Schenke
orcid: 0000-0001-5427-9527
- first_name: Hendrik
full_name: Vater, Hendrik
id: '63220'
last_name: Vater
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Daniel
full_name: Weber, Daniel
id: '24041'
last_name: Weber
orcid: 0000-0003-3367-5998
citation:
ama: 'Abdelwanis AHA, Haucke-Korber B, Jakobeit D, et al. Reinforcement Learning:
A Comprehensive Open-Source Course. Journal of Open Source Education. 2026;9(97).
doi:10.21105/jose.00306'
apa: 'Abdelwanis, A. H. A., Haucke-Korber, B., Jakobeit, D., Kirchgässner, W., Meyer,
M., Schenke, M., Vater, H., Wallscheid, O., & Weber, D. (2026). Reinforcement
Learning: A Comprehensive Open-Source Course. Journal of Open Source Education,
9(97), Article 306. https://doi.org/10.21105/jose.00306'
bibtex: '@article{Abdelwanis_Haucke-Korber_Jakobeit_Kirchgässner_Meyer_Schenke_Vater_Wallscheid_Weber_2026,
title={Reinforcement Learning: A Comprehensive Open-Source Course}, volume={9},
DOI={10.21105/jose.00306}, number={97306},
journal={Journal of Open Source Education}, publisher={The Open Journal}, author={Abdelwanis,
Ali Hassan Ali and Haucke-Korber, Barnabas and Jakobeit, Darius and Kirchgässner,
Wilhelm and Meyer, Marvin and Schenke, Maximilian and Vater, Hendrik and Wallscheid,
Oliver and Weber, Daniel}, year={2026} }'
chicago: 'Abdelwanis, Ali Hassan Ali, Barnabas Haucke-Korber, Darius Jakobeit, Wilhelm
Kirchgässner, Marvin Meyer, Maximilian Schenke, Hendrik Vater, Oliver Wallscheid,
and Daniel Weber. “Reinforcement Learning: A Comprehensive Open-Source Course.”
Journal of Open Source Education 9, no. 97 (2026). https://doi.org/10.21105/jose.00306.'
ieee: 'A. H. A. Abdelwanis et al., “Reinforcement Learning: A Comprehensive
Open-Source Course,” Journal of Open Source Education, vol. 9, no. 97,
Art. no. 306, 2026, doi: 10.21105/jose.00306.'
mla: 'Abdelwanis, Ali Hassan Ali, et al. “Reinforcement Learning: A Comprehensive
Open-Source Course.” Journal of Open Source Education, vol. 9, no. 97,
306, The Open Journal, 2026, doi:10.21105/jose.00306.'
short: A.H.A. Abdelwanis, B. Haucke-Korber, D. Jakobeit, W. Kirchgässner, M. Meyer,
M. Schenke, H. Vater, O. Wallscheid, D. Weber, Journal of Open Source Education
9 (2026).
date_created: 2026-03-31T07:30:04Z
date_updated: 2026-03-31T07:32:23Z
department:
- _id: '52'
doi: 10.21105/jose.00306
intvolume: ' 9'
issue: '97'
language:
- iso: eng
publication: Journal of Open Source Education
publication_identifier:
issn:
- 2577-3569
publication_status: published
publisher: The Open Journal
status: public
title: 'Reinforcement Learning: A Comprehensive Open-Source Course'
type: journal_article
user_id: '93461'
volume: 9
year: '2026'
...
---
_id: '58756'
abstract:
- lang: eng
text: Der Permanentmagnet-Synchronmotor (PMSM) ist aufgrund seiner hohen Leistungs-
und Drehmomentdichte bezogen auf Volumen und Gewicht ein häufig verwendeter Traktionsmotor
in Automobilanwendungen. Jene Charakteristika werden jedoch maßgeblich durch Temperaturhöchstwerte
begrenzt. Hinzu kommt, dass die Temperatur wichtiger Rotorkomponenten nicht wirtschaftlich
messbar ist. Temperaturschätzverfahren wie modellbasierte Ansätze sind potentiell
in der Lage, das Problem der fehlenden Temperaturinformation zu relativieren,
ohne zusätzliche Geräte zu erfordern. Diese Arbeit stellt ein Portfolio von thermischen
Modellen aus dem Bereich des maschinellen Lernens zusammen. Die Untersuchung basiert
auf einem PMSM-Datensatz, der auf einem Prüfstand aufgezeichnet wurde. Neben dem
durchschnittlichen Schätzfehler diktiert die erforderliche Anzahl von Modellparametern
zahlreiche Auslegungsentscheidungen. Der gesamte Entwurfsprozess eines Modells
aus dem maschinellen Lernen wird beleuchtet und für verschiedene lineare, sowie
baumbasierte Modelle; vorschiebende, rekurrente und faltende neuronale Netze als
auch für verschiedene hybride Modellierungsansätze durchgeführt. Desweiteren wird
der hybride Modellierungsansatz über thermische neuronale Netze besonders hervorgehoben.
Sie setzen sich aus neuronalen Netzen und einem thermischen Ersatzschaltbild zusammen
und wurden erstmals vom Autor dieser Arbeit veröffentlicht. Schließlich wird ein
von Experten entworfenes, datengetriebenes thermisches Netz mit konzentrierten
Parametern über verschiedene Algorithmen optimiert und als Stand der Technik herangezogen.
- lang: eng
text: The permanent magnet synchronous motor (PMSM) is a commonly used traction
motor in automotive applications due to its high power and torque density with
respect to volume and weight. These characteristics are constrained by the maximum
temperature at which vital components can still operate without harm. Moreover,
important rotor component temperatures cannot be measured economically. Temperature
estimation methods such as model-based approaches can alleviate the problem of
missing thermal information at potentially no additionally required equipment.
This work collates a portfolio of data-driven thermal models from the domain of
machine learning and investigates their feasibility for the task of accurate thermal
modeling on the example of a PMSM data set recorded on a test bench. Aside from
the average estimation error, the required amount of model parameters as an approximation
for the computational demand dictates design decisions throughout. The whole process
of designing a machine learning model is illuminated and carried out for varying
linear models; tree-based models; feed-forward, recurrent, and convolutional neural
networks, as well as various hybrid gray-box modeling approaches. Moreover, a
hybrid modeling paradigm with thermal neural networks is highlighted, which was
first introduced by this work's author. Eventually, an expert-designed, data-driven
lumped-parameter thermal network is optimized under different algorithms in order
to put machine learning models to the test against the state of the art of thermal
modeling.
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
citation:
ama: Kirchgässner W. Data-Driven Thermal Modeling of a Permanent Magnet Synchronous
Motor with Machine Learning. LibreCat University; 2024. doi:10.17619/UNIPB/1-2068
apa: Kirchgässner, W. (2024). Data-driven thermal modeling of a permanent magnet
synchronous motor with machine learning. LibreCat University. https://doi.org/10.17619/UNIPB/1-2068
bibtex: '@book{Kirchgässner_2024, title={Data-driven thermal modeling of a permanent
magnet synchronous motor with machine learning}, DOI={10.17619/UNIPB/1-2068},
publisher={LibreCat University}, author={Kirchgässner, Wilhelm}, year={2024} }'
chicago: Kirchgässner, Wilhelm. Data-Driven Thermal Modeling of a Permanent Magnet
Synchronous Motor with Machine Learning. LibreCat University, 2024. https://doi.org/10.17619/UNIPB/1-2068.
ieee: W. Kirchgässner, Data-driven thermal modeling of a permanent magnet synchronous
motor with machine learning. LibreCat University, 2024.
mla: Kirchgässner, Wilhelm. Data-Driven Thermal Modeling of a Permanent Magnet
Synchronous Motor with Machine Learning. LibreCat University, 2024, doi:10.17619/UNIPB/1-2068.
short: W. Kirchgässner, Data-Driven Thermal Modeling of a Permanent Magnet Synchronous
Motor with Machine Learning, LibreCat University, 2024.
date_created: 2025-02-21T11:38:22Z
date_updated: 2025-02-21T11:41:01Z
department:
- _id: '52'
doi: 10.17619/UNIPB/1-2068
language:
- iso: eng
publisher: LibreCat University
status: public
title: Data-driven thermal modeling of a permanent magnet synchronous motor with machine
learning
type: dissertation
user_id: '71353'
year: '2024'
...
---
_id: '34065'
article_number: '105537'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wallscheid O, Böcker J. Thermal neural networks: Lumped-parameter
thermal modeling with state-space machine learning. Engineering Applications
of Artificial Intelligence. 2022;117. doi:10.1016/j.engappai.2022.105537'
apa: 'Kirchgässner, W., Wallscheid, O., & Böcker, J. (2022). Thermal neural
networks: Lumped-parameter thermal modeling with state-space machine learning.
Engineering Applications of Artificial Intelligence, 117, Article
105537. https://doi.org/10.1016/j.engappai.2022.105537'
bibtex: '@article{Kirchgässner_Wallscheid_Böcker_2022, title={Thermal neural networks:
Lumped-parameter thermal modeling with state-space machine learning}, volume={117},
DOI={10.1016/j.engappai.2022.105537},
number={105537}, journal={Engineering Applications of Artificial Intelligence},
publisher={Elsevier BV}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver
and Böcker, Joachim}, year={2022} }'
chicago: 'Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Thermal
Neural Networks: Lumped-Parameter Thermal Modeling with State-Space Machine Learning.”
Engineering Applications of Artificial Intelligence 117 (2022). https://doi.org/10.1016/j.engappai.2022.105537.'
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Thermal neural networks:
Lumped-parameter thermal modeling with state-space machine learning,” Engineering
Applications of Artificial Intelligence, vol. 117, Art. no. 105537, 2022,
doi: 10.1016/j.engappai.2022.105537.'
mla: 'Kirchgässner, Wilhelm, et al. “Thermal Neural Networks: Lumped-Parameter Thermal
Modeling with State-Space Machine Learning.” Engineering Applications of Artificial
Intelligence, vol. 117, 105537, Elsevier BV, 2022, doi:10.1016/j.engappai.2022.105537.'
short: W. Kirchgässner, O. Wallscheid, J. Böcker, Engineering Applications of Artificial
Intelligence 117 (2022).
date_created: 2022-11-14T08:13:11Z
date_updated: 2023-03-09T10:08:12Z
department:
- _id: '52'
doi: 10.1016/j.engappai.2022.105537
intvolume: ' 117'
language:
- iso: eng
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Engineering Applications of Artificial Intelligence
publication_identifier:
issn:
- 0952-1976
publication_status: published
publisher: Elsevier BV
status: public
title: 'Thermal neural networks: Lumped-parameter thermal modeling with state-space
machine learning'
type: journal_article
user_id: '49265'
volume: 117
year: '2022'
...
---
_id: '32859'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wallscheid O, Böcker J. Learning Thermal Properties and Temperature
Models of Electric Motors with Neural Ordinary Differential Equations. In: 2022
International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia).
IEEE; 2022. doi:10.23919/ipec-himeji2022-ecce53331.2022.9807209'
apa: Kirchgässner, W., Wallscheid, O., & Böcker, J. (2022). Learning Thermal
Properties and Temperature Models of Electric Motors with Neural Ordinary Differential
Equations. 2022 International Power Electronics Conference (IPEC-Himeji 2022-
ECCE Asia). https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209
bibtex: '@inproceedings{Kirchgässner_Wallscheid_Böcker_2022, title={Learning Thermal
Properties and Temperature Models of Electric Motors with Neural Ordinary Differential
Equations}, DOI={10.23919/ipec-himeji2022-ecce53331.2022.9807209},
booktitle={2022 International Power Electronics Conference (IPEC-Himeji 2022-
ECCE Asia)}, publisher={IEEE}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver
and Böcker, Joachim}, year={2022} }'
chicago: Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Learning
Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary
Differential Equations.” In 2022 International Power Electronics Conference
(IPEC-Himeji 2022- ECCE Asia). IEEE, 2022. https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209.
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Learning Thermal Properties
and Temperature Models of Electric Motors with Neural Ordinary Differential Equations,”
2022, doi: 10.23919/ipec-himeji2022-ecce53331.2022.9807209.'
mla: Kirchgässner, Wilhelm, et al. “Learning Thermal Properties and Temperature
Models of Electric Motors with Neural Ordinary Differential Equations.” 2022
International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia),
IEEE, 2022, doi:10.23919/ipec-himeji2022-ecce53331.2022.9807209.
short: 'W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2022 International Power
Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, 2022.'
date_created: 2022-08-16T15:38:35Z
date_updated: 2023-03-09T10:08:29Z
department:
- _id: '52'
doi: 10.23919/ipec-himeji2022-ecce53331.2022.9807209
language:
- iso: eng
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE
Asia)
publication_status: published
publisher: IEEE
status: public
title: Learning Thermal Properties and Temperature Models of Electric Motors with
Neural Ordinary Differential Equations
type: conference
user_id: '49265'
year: '2022'
...
---
_id: '42894'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Daniel
full_name: Wöckinger, Daniel
last_name: Wöckinger
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Gerd
full_name: Bramerdorfer, Gerd
last_name: Bramerdorfer
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wöckinger D, Wallscheid O, Bramerdorfer G, Böcker J. Application
of Thermal Neural Networks on a Small-Scale Electric Motor. In: IKMT 2022;
13. GMM/ETG-Symposium. ; 2022:1-6.'
apa: Kirchgässner, W., Wöckinger, D., Wallscheid, O., Bramerdorfer, G., & Böcker,
J. (2022). Application of Thermal Neural Networks on a Small-Scale Electric Motor.
IKMT 2022; 13. GMM/ETG-Symposium, 1–6.
bibtex: '@inproceedings{Kirchgässner_Wöckinger_Wallscheid_Bramerdorfer_Böcker_2022,
title={Application of Thermal Neural Networks on a Small-Scale Electric Motor},
booktitle={IKMT 2022; 13. GMM/ETG-Symposium}, author={Kirchgässner, Wilhelm and
Wöckinger, Daniel and Wallscheid, Oliver and Bramerdorfer, Gerd and Böcker, Joachim},
year={2022}, pages={1–6} }'
chicago: Kirchgässner, Wilhelm, Daniel Wöckinger, Oliver Wallscheid, Gerd Bramerdorfer,
and Joachim Böcker. “Application of Thermal Neural Networks on a Small-Scale Electric
Motor.” In IKMT 2022; 13. GMM/ETG-Symposium, 1–6, 2022.
ieee: W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, and J. Böcker,
“Application of Thermal Neural Networks on a Small-Scale Electric Motor,” in IKMT
2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.
mla: Kirchgässner, Wilhelm, et al. “Application of Thermal Neural Networks on a
Small-Scale Electric Motor.” IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp.
1–6.
short: 'W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, J. Böcker,
in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.'
date_created: 2023-03-09T12:10:01Z
date_updated: 2023-03-09T12:11:43Z
department:
- _id: '52'
language:
- iso: eng
page: 1-6
publication: IKMT 2022; 13. GMM/ETG-Symposium
status: public
title: Application of Thermal Neural Networks on a Small-Scale Electric Motor
type: conference
user_id: '49265'
year: '2022'
...
---
_id: '22162'
author:
- first_name: Gerrit
full_name: Book, Gerrit
last_name: Book
- first_name: Arne
full_name: Traue, Arne
last_name: Traue
- first_name: Praneeth
full_name: Balakrishna, Praneeth
last_name: Balakrishna
- first_name: Anian
full_name: Brosch, Anian
id: '75779'
last_name: Brosch
orcid: 0000-0003-4871-1664
- first_name: Maximilian
full_name: Schenke, Maximilian
id: '52638'
last_name: Schenke
orcid: 0000-0001-5427-9527
- first_name: Sören
full_name: Hanke, Sören
id: '25027'
last_name: Hanke
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
citation:
ama: Book G, Traue A, Balakrishna P, et al. Transferring Online Reinforcement Learning
for Electric Motor Control From Simulation to Real-World Experiments. IEEE
Open Journal of Power Electronics. Published online 2021:187-201. doi:10.1109/ojpel.2021.3065877
apa: Book, G., Traue, A., Balakrishna, P., Brosch, A., Schenke, M., Hanke, S., Kirchgässner,
W., & Wallscheid, O. (2021). Transferring Online Reinforcement Learning for
Electric Motor Control From Simulation to Real-World Experiments. IEEE Open
Journal of Power Electronics, 187–201. https://doi.org/10.1109/ojpel.2021.3065877
bibtex: '@article{Book_Traue_Balakrishna_Brosch_Schenke_Hanke_Kirchgässner_Wallscheid_2021,
title={Transferring Online Reinforcement Learning for Electric Motor Control From
Simulation to Real-World Experiments}, DOI={10.1109/ojpel.2021.3065877},
journal={IEEE Open Journal of Power Electronics}, author={Book, Gerrit and Traue,
Arne and Balakrishna, Praneeth and Brosch, Anian and Schenke, Maximilian and Hanke,
Sören and Kirchgässner, Wilhelm and Wallscheid, Oliver}, year={2021}, pages={187–201}
}'
chicago: Book, Gerrit, Arne Traue, Praneeth Balakrishna, Anian Brosch, Maximilian
Schenke, Sören Hanke, Wilhelm Kirchgässner, and Oliver Wallscheid. “Transferring
Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World
Experiments.” IEEE Open Journal of Power Electronics, 2021, 187–201. https://doi.org/10.1109/ojpel.2021.3065877.
ieee: 'G. Book et al., “Transferring Online Reinforcement Learning for Electric
Motor Control From Simulation to Real-World Experiments,” IEEE Open Journal
of Power Electronics, pp. 187–201, 2021, doi: 10.1109/ojpel.2021.3065877.'
mla: Book, Gerrit, et al. “Transferring Online Reinforcement Learning for Electric
Motor Control From Simulation to Real-World Experiments.” IEEE Open Journal
of Power Electronics, 2021, pp. 187–201, doi:10.1109/ojpel.2021.3065877.
short: G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner,
O. Wallscheid, IEEE Open Journal of Power Electronics (2021) 187–201.
date_created: 2021-05-12T16:54:27Z
date_updated: 2022-02-22T08:51:05Z
department:
- _id: '52'
doi: 10.1109/ojpel.2021.3065877
language:
- iso: eng
page: 187-201
publication: IEEE Open Journal of Power Electronics
publication_identifier:
issn:
- 2644-1314
publication_status: published
status: public
title: Transferring Online Reinforcement Learning for Electric Motor Control From
Simulation to Real-World Experiments
type: journal_article
user_id: '66'
year: '2021'
...
---
_id: '21251'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wallscheid O, Böcker J. Data-Driven Permanent Magnet Temperature
Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark.
IEEE Transactions on Energy Conversion. 2021;36(3):2059-2067. doi:10.1109/tec.2021.3052546'
apa: 'Kirchgässner, W., Wallscheid, O., & Böcker, J. (2021). Data-Driven Permanent
Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning:
A Benchmark. IEEE Transactions on Energy Conversion, 36(3), 2059–2067.
https://doi.org/10.1109/tec.2021.3052546'
bibtex: '@article{Kirchgässner_Wallscheid_Böcker_2021, title={Data-Driven Permanent
Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning:
A Benchmark}, volume={36}, DOI={10.1109/tec.2021.3052546},
number={3}, journal={IEEE Transactions on Energy Conversion}, author={Kirchgässner,
Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2021}, pages={2059–2067}
}'
chicago: 'Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Data-Driven
Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised
Machine Learning: A Benchmark.” IEEE Transactions on Energy Conversion
36, no. 3 (2021): 2059–67. https://doi.org/10.1109/tec.2021.3052546.'
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Data-Driven Permanent Magnet
Temperature Estimation in Synchronous Motors with Supervised Machine Learning:
A Benchmark,” IEEE Transactions on Energy Conversion, vol. 36, no. 3, pp.
2059–2067, 2021, doi: 10.1109/tec.2021.3052546.'
mla: 'Kirchgässner, Wilhelm, et al. “Data-Driven Permanent Magnet Temperature Estimation
in Synchronous Motors with Supervised Machine Learning: A Benchmark.” IEEE
Transactions on Energy Conversion, vol. 36, no. 3, 2021, pp. 2059–67, doi:10.1109/tec.2021.3052546.'
short: W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Energy Conversion
36 (2021) 2059–2067.
date_created: 2021-02-16T21:22:46Z
date_updated: 2022-02-25T20:31:46Z
department:
- _id: '52'
doi: 10.1109/tec.2021.3052546
intvolume: ' 36'
issue: '3'
language:
- iso: eng
page: 2059 - 2067
publication: IEEE Transactions on Energy Conversion
publication_identifier:
issn:
- 0885-8969
- 1558-0059
publication_status: published
status: public
title: 'Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors
with Supervised Machine Learning: A Benchmark'
type: journal_article
user_id: '11291'
volume: 36
year: '2021'
...
---
_id: '21254'
article_number: '2498'
author:
- first_name: Praneeth
full_name: Balakrishna, Praneeth
last_name: Balakrishna
- first_name: Gerrit
full_name: Book, Gerrit
last_name: Book
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Maximilian
full_name: Schenke, Maximilian
id: '52638'
last_name: Schenke
orcid: 0000-0001-5427-9527
- first_name: Arne
full_name: Traue, Arne
last_name: Traue
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
citation:
ama: 'Balakrishna P, Book G, Kirchgässner W, Schenke M, Traue A, Wallscheid O. gym-electric-motor
(GEM): A Python toolbox for the simulation of electric drive systems. Journal
of Open Source Software. Published online 2021. doi:10.21105/joss.02498'
apa: 'Balakrishna, P., Book, G., Kirchgässner, W., Schenke, M., Traue, A., &
Wallscheid, O. (2021). gym-electric-motor (GEM): A Python toolbox for the simulation
of electric drive systems. Journal of Open Source Software, Article 2498.
https://doi.org/10.21105/joss.02498'
bibtex: '@article{Balakrishna_Book_Kirchgässner_Schenke_Traue_Wallscheid_2021, title={gym-electric-motor
(GEM): A Python toolbox for the simulation of electric drive systems}, DOI={10.21105/joss.02498}, number={2498},
journal={Journal of Open Source Software}, author={Balakrishna, Praneeth and Book,
Gerrit and Kirchgässner, Wilhelm and Schenke, Maximilian and Traue, Arne and Wallscheid,
Oliver}, year={2021} }'
chicago: 'Balakrishna, Praneeth, Gerrit Book, Wilhelm Kirchgässner, Maximilian Schenke,
Arne Traue, and Oliver Wallscheid. “Gym-Electric-Motor (GEM): A Python Toolbox
for the Simulation of Electric Drive Systems.” Journal of Open Source Software,
2021. https://doi.org/10.21105/joss.02498.'
ieee: 'P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, and O. Wallscheid,
“gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive
systems,” Journal of Open Source Software, Art. no. 2498, 2021, doi: 10.21105/joss.02498.'
mla: 'Balakrishna, Praneeth, et al. “Gym-Electric-Motor (GEM): A Python Toolbox
for the Simulation of Electric Drive Systems.” Journal of Open Source Software,
2498, 2021, doi:10.21105/joss.02498.'
short: P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, O. Wallscheid,
Journal of Open Source Software (2021).
date_created: 2021-02-16T21:40:12Z
date_updated: 2022-02-25T20:31:36Z
department:
- _id: '52'
doi: 10.21105/joss.02498
language:
- iso: eng
publication: Journal of Open Source Software
publication_identifier:
issn:
- 2475-9066
publication_status: published
status: public
title: 'gym-electric-motor (GEM): A Python toolbox for the simulation of electric
drive systems'
type: journal_article
user_id: '11291'
year: '2021'
...
---
_id: '29655'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wallscheid O, Böcker J. Thermal Neural Networks: Lumped-Parameter
Thermal Modeling With State-Space Machine Learning. arXiv preprint arXiv:210316323.
Published online 2021.'
apa: 'Kirchgässner, W., Wallscheid, O., & Böcker, J. (2021). Thermal Neural
Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning.
In arXiv preprint arXiv:2103.16323.'
bibtex: '@article{Kirchgässner_Wallscheid_Böcker_2021, title={Thermal Neural Networks:
Lumped-Parameter Thermal Modeling With State-Space Machine Learning}, journal={arXiv
preprint arXiv:2103.16323}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver
and Böcker, Joachim}, year={2021} }'
chicago: 'Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Thermal
Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning.”
ArXiv Preprint ArXiv:2103.16323, 2021.'
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Thermal Neural Networks:
Lumped-Parameter Thermal Modeling With State-Space Machine Learning,” arXiv
preprint arXiv:2103.16323. 2021.'
mla: 'Kirchgässner, Wilhelm, et al. “Thermal Neural Networks: Lumped-Parameter Thermal
Modeling With State-Space Machine Learning.” ArXiv Preprint ArXiv:2103.16323,
2021.'
short: W. Kirchgässner, O. Wallscheid, J. Böcker, ArXiv Preprint ArXiv:2103.16323
(2021).
date_created: 2022-01-28T14:11:06Z
date_updated: 2023-03-09T10:11:13Z
department:
- _id: '52'
language:
- iso: eng
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: arXiv preprint arXiv:2103.16323
status: public
title: 'Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space
Machine Learning'
type: preprint
user_id: '49265'
year: '2021'
...
---
_id: '21252'
author:
- first_name: Arne
full_name: Traue, Arne
last_name: Traue
- first_name: Gerrit
full_name: Book, Gerrit
last_name: Book
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
citation:
ama: Traue A, Book G, Kirchgässner W, Wallscheid O. Toward a Reinforcement Learning
Environment Toolbox for Intelligent Electric Motor Control. IEEE Transactions
on Neural Networks and Learning Systems. Published online 2020:1-10. doi:10.1109/tnnls.2020.3029573
apa: Traue, A., Book, G., Kirchgässner, W., & Wallscheid, O. (2020). Toward
a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control.
IEEE Transactions on Neural Networks and Learning Systems, 1–10. https://doi.org/10.1109/tnnls.2020.3029573
bibtex: '@article{Traue_Book_Kirchgässner_Wallscheid_2020, title={Toward a Reinforcement
Learning Environment Toolbox for Intelligent Electric Motor Control}, DOI={10.1109/tnnls.2020.3029573},
journal={IEEE Transactions on Neural Networks and Learning Systems}, author={Traue,
Arne and Book, Gerrit and Kirchgässner, Wilhelm and Wallscheid, Oliver}, year={2020},
pages={1–10} }'
chicago: Traue, Arne, Gerrit Book, Wilhelm Kirchgässner, and Oliver Wallscheid.
“Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric
Motor Control.” IEEE Transactions on Neural Networks and Learning Systems,
2020, 1–10. https://doi.org/10.1109/tnnls.2020.3029573.
ieee: 'A. Traue, G. Book, W. Kirchgässner, and O. Wallscheid, “Toward a Reinforcement
Learning Environment Toolbox for Intelligent Electric Motor Control,” IEEE
Transactions on Neural Networks and Learning Systems, pp. 1–10, 2020, doi:
10.1109/tnnls.2020.3029573.'
mla: Traue, Arne, et al. “Toward a Reinforcement Learning Environment Toolbox for
Intelligent Electric Motor Control.” IEEE Transactions on Neural Networks and
Learning Systems, 2020, pp. 1–10, doi:10.1109/tnnls.2020.3029573.
short: A. Traue, G. Book, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Neural
Networks and Learning Systems (2020) 1–10.
date_created: 2021-02-16T21:23:53Z
date_updated: 2022-02-18T13:26:18Z
department:
- _id: '52'
doi: 10.1109/tnnls.2020.3029573
language:
- iso: eng
page: 1-10
publication: IEEE Transactions on Neural Networks and Learning Systems
publication_identifier:
issn:
- 2162-237X
- 2162-2388
publication_status: published
status: public
title: Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric
Motor Control
type: journal_article
user_id: '49265'
year: '2020'
...
---
_id: '21250'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: Kirchgässner W, Wallscheid O, Böcker J. Estimating Electric Motor Temperatures
with Deep Residual Machine Learning. IEEE Transactions on Power Electronics.
2020;36(7):7480-7488. doi:10.1109/tpel.2020.3045596
apa: Kirchgässner, W., Wallscheid, O., & Böcker, J. (2020). Estimating Electric
Motor Temperatures with Deep Residual Machine Learning. IEEE Transactions on
Power Electronics, 36(7), 7480–7488. https://doi.org/10.1109/tpel.2020.3045596
bibtex: '@article{Kirchgässner_Wallscheid_Böcker_2020, title={Estimating Electric
Motor Temperatures with Deep Residual Machine Learning}, volume={36}, DOI={10.1109/tpel.2020.3045596},
number={7}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner,
Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2020}, pages={7480–7488}
}'
chicago: 'Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Estimating
Electric Motor Temperatures with Deep Residual Machine Learning.” IEEE Transactions
on Power Electronics 36, no. 7 (2020): 7480–88. https://doi.org/10.1109/tpel.2020.3045596.'
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Estimating Electric Motor
Temperatures with Deep Residual Machine Learning,” IEEE Transactions on Power
Electronics, vol. 36, no. 7, pp. 7480–7488, 2020, doi: 10.1109/tpel.2020.3045596.'
mla: Kirchgässner, Wilhelm, et al. “Estimating Electric Motor Temperatures with
Deep Residual Machine Learning.” IEEE Transactions on Power Electronics,
vol. 36, no. 7, 2020, pp. 7480–88, doi:10.1109/tpel.2020.3045596.
short: W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics
36 (2020) 7480–7488.
date_created: 2021-02-16T21:22:32Z
date_updated: 2022-02-19T16:51:20Z
department:
- _id: '52'
doi: 10.1109/tpel.2020.3045596
intvolume: ' 36'
issue: '7'
language:
- iso: eng
page: 7480-7488
publication: IEEE Transactions on Power Electronics
publication_identifier:
issn:
- 0885-8993
- 1941-0107
publication_status: published
status: public
title: Estimating Electric Motor Temperatures with Deep Residual Machine Learning
type: journal_article
user_id: '49265'
volume: 36
year: '2020'
...
---
_id: '29640'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: Kirchgässner W, Wallscheid O, Böcker J. Data-Driven Permanent Magnet Temperature
Estimation in Synchronous Motors with Supervised Machine Learning. arXiv preprint
arXiv:200106246. Published online 2020.
apa: Kirchgässner, W., Wallscheid, O., & Böcker, J. (2020). Data-Driven Permanent
Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning.
ArXiv Preprint ArXiv:2001.06246.
bibtex: '@article{Kirchgässner_Wallscheid_Böcker_2020, title={Data-Driven Permanent
Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning},
journal={arXiv preprint arXiv:2001.06246}, author={Kirchgässner, Wilhelm and Wallscheid,
Oliver and Böcker, Joachim}, year={2020} }'
chicago: Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Data-Driven
Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised
Machine Learning.” ArXiv Preprint ArXiv:2001.06246, 2020.
ieee: W. Kirchgässner, O. Wallscheid, and J. Böcker, “Data-Driven Permanent Magnet
Temperature Estimation in Synchronous Motors with Supervised Machine Learning,”
arXiv preprint arXiv:2001.06246, 2020.
mla: Kirchgässner, Wilhelm, et al. “Data-Driven Permanent Magnet Temperature Estimation
in Synchronous Motors with Supervised Machine Learning.” ArXiv Preprint ArXiv:2001.06246,
2020.
short: W. Kirchgässner, O. Wallscheid, J. Böcker, ArXiv Preprint ArXiv:2001.06246
(2020).
date_created: 2022-01-28T14:11:02Z
date_updated: 2022-03-17T07:23:10Z
department:
- _id: '52'
language:
- iso: eng
publication: arXiv preprint arXiv:2001.06246
status: public
title: Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with
Supervised Machine Learning
type: journal_article
user_id: '11291'
year: '2020'
...
---
_id: '25030'
author:
- first_name: Maximilian
full_name: Schenke, Maximilian
id: '52638'
last_name: Schenke
orcid: 0000-0001-5427-9527
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
citation:
ama: 'Schenke M, Kirchgässner W, Wallscheid O. Controller Design for Electrical
Drives by Deep Reinforcement Learning: A Proof of Concept. IEEE Transactions
on Industrial Informatics. Published online 2019:4650-4658. doi:10.1109/tii.2019.2948387'
apa: 'Schenke, M., Kirchgässner, W., & Wallscheid, O. (2019). Controller Design
for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept. IEEE
Transactions on Industrial Informatics, 4650–4658. https://doi.org/10.1109/tii.2019.2948387'
bibtex: '@article{Schenke_Kirchgässner_Wallscheid_2019, title={Controller Design
for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept}, DOI={10.1109/tii.2019.2948387},
journal={IEEE Transactions on Industrial Informatics}, author={Schenke, Maximilian
and Kirchgässner, Wilhelm and Wallscheid, Oliver}, year={2019}, pages={4650–4658}
}'
chicago: 'Schenke, Maximilian, Wilhelm Kirchgässner, and Oliver Wallscheid. “Controller
Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept.”
IEEE Transactions on Industrial Informatics, 2019, 4650–58. https://doi.org/10.1109/tii.2019.2948387.'
ieee: 'M. Schenke, W. Kirchgässner, and O. Wallscheid, “Controller Design for Electrical
Drives by Deep Reinforcement Learning: A Proof of Concept,” IEEE Transactions
on Industrial Informatics, pp. 4650–4658, 2019, doi: 10.1109/tii.2019.2948387.'
mla: 'Schenke, Maximilian, et al. “Controller Design for Electrical Drives by Deep
Reinforcement Learning: A Proof of Concept.” IEEE Transactions on Industrial
Informatics, 2019, pp. 4650–58, doi:10.1109/tii.2019.2948387.'
short: M. Schenke, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Industrial
Informatics (2019) 4650–4658.
date_created: 2021-09-24T10:30:54Z
date_updated: 2022-02-15T09:02:10Z
department:
- _id: '52'
doi: 10.1109/tii.2019.2948387
language:
- iso: eng
page: 4650-4658
publication: IEEE Transactions on Industrial Informatics
publication_identifier:
issn:
- 1551-3203
- 1941-0050
publication_status: published
status: public
title: 'Controller Design for Electrical Drives by Deep Reinforcement Learning: A
Proof of Concept'
type: journal_article
user_id: '52638'
year: '2019'
...
---
_id: '21247'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wallscheid O, Böcker J. Empirical Evaluation of Exponentially
Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet
Synchronous Machines. In: 2019 IEEE 28th International Symposium on Industrial
Electronics (ISIE). ; 2019. doi:10.1109/isie.2019.8781195'
apa: Kirchgässner, W., Wallscheid, O., & Böcker, J. (2019). Empirical Evaluation
of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of
Permanent Magnet Synchronous Machines. 2019 IEEE 28th International Symposium
on Industrial Electronics (ISIE). https://doi.org/10.1109/isie.2019.8781195
bibtex: '@inproceedings{Kirchgässner_Wallscheid_Böcker_2019, title={Empirical Evaluation
of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of
Permanent Magnet Synchronous Machines}, DOI={10.1109/isie.2019.8781195},
booktitle={2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)},
author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2019}
}'
chicago: Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Empirical
Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal
Modeling of Permanent Magnet Synchronous Machines.” In 2019 IEEE 28th International
Symposium on Industrial Electronics (ISIE), 2019. https://doi.org/10.1109/isie.2019.8781195.
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Empirical Evaluation of Exponentially
Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet
Synchronous Machines,” 2019, doi: 10.1109/isie.2019.8781195.'
mla: Kirchgässner, Wilhelm, et al. “Empirical Evaluation of Exponentially Weighted
Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous
Machines.” 2019 IEEE 28th International Symposium on Industrial Electronics
(ISIE), 2019, doi:10.1109/isie.2019.8781195.
short: 'W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE 28th International
Symposium on Industrial Electronics (ISIE), 2019.'
date_created: 2021-02-16T21:21:21Z
date_updated: 2022-02-19T16:51:45Z
department:
- _id: '52'
doi: 10.1109/isie.2019.8781195
language:
- iso: eng
publication: 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)
publication_identifier:
isbn:
- '9781728136660'
publication_status: published
status: public
title: Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear
Thermal Modeling of Permanent Magnet Synchronous Machines
type: conference
user_id: '49265'
year: '2019'
...
---
_id: '21249'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Kirchgässner W, Wallscheid O, Böcker J. Deep Residual Convolutional and Recurrent
Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors.
In: 2019 IEEE International Electric Machines & Drives Conference (IEMDC).
; 2019. doi:10.1109/iemdc.2019.8785109'
apa: Kirchgässner, W., Wallscheid, O., & Böcker, J. (2019). Deep Residual Convolutional
and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous
Motors. 2019 IEEE International Electric Machines & Drives Conference (IEMDC).
https://doi.org/10.1109/iemdc.2019.8785109
bibtex: '@inproceedings{Kirchgässner_Wallscheid_Böcker_2019, title={Deep Residual
Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent
Magnet Synchronous Motors}, DOI={10.1109/iemdc.2019.8785109},
booktitle={2019 IEEE International Electric Machines & Drives Conference (IEMDC)},
author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2019}
}'
chicago: Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Deep Residual
Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent
Magnet Synchronous Motors.” In 2019 IEEE International Electric Machines &
Drives Conference (IEMDC), 2019. https://doi.org/10.1109/iemdc.2019.8785109.
ieee: 'W. Kirchgässner, O. Wallscheid, and J. Böcker, “Deep Residual Convolutional
and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous
Motors,” 2019, doi: 10.1109/iemdc.2019.8785109.'
mla: Kirchgässner, Wilhelm, et al. “Deep Residual Convolutional and Recurrent Neural
Networks for Temperature Estimation in Permanent Magnet Synchronous Motors.” 2019
IEEE International Electric Machines & Drives Conference (IEMDC), 2019,
doi:10.1109/iemdc.2019.8785109.
short: 'W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE International Electric
Machines & Drives Conference (IEMDC), 2019.'
date_created: 2021-02-16T21:22:06Z
date_updated: 2022-02-19T16:51:49Z
department:
- _id: '52'
doi: 10.1109/iemdc.2019.8785109
language:
- iso: eng
publication: 2019 IEEE International Electric Machines & Drives Conference (IEMDC)
publication_identifier:
isbn:
- '9781538693506'
publication_status: published
status: public
title: Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation
in Permanent Magnet Synchronous Motors
type: conference
user_id: '49265'
year: '2019'
...
---
_id: '21248'
author:
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
id: '49265'
last_name: Kirchgässner
orcid: 0000-0001-9490-1843
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Wallscheid O, Kirchgässner W, Böcker J. Investigation of long short-term memory
networks to temperature prediction for permanent magnet synchronous motors. In:
2017 International Joint Conference on Neural Networks (IJCNN). ; 2017.
doi:10.1109/ijcnn.2017.7966088'
apa: Wallscheid, O., Kirchgässner, W., & Böcker, J. (2017). Investigation of
long short-term memory networks to temperature prediction for permanent magnet
synchronous motors. 2017 International Joint Conference on Neural Networks
(IJCNN). https://doi.org/10.1109/ijcnn.2017.7966088
bibtex: '@inproceedings{Wallscheid_Kirchgässner_Böcker_2017, title={Investigation
of long short-term memory networks to temperature prediction for permanent magnet
synchronous motors}, DOI={10.1109/ijcnn.2017.7966088},
booktitle={2017 International Joint Conference on Neural Networks (IJCNN)}, author={Wallscheid,
Oliver and Kirchgässner, Wilhelm and Böcker, Joachim}, year={2017} }'
chicago: Wallscheid, Oliver, Wilhelm Kirchgässner, and Joachim Böcker. “Investigation
of Long Short-Term Memory Networks to Temperature Prediction for Permanent Magnet
Synchronous Motors.” In 2017 International Joint Conference on Neural Networks
(IJCNN), 2017. https://doi.org/10.1109/ijcnn.2017.7966088.
ieee: 'O. Wallscheid, W. Kirchgässner, and J. Böcker, “Investigation of long short-term
memory networks to temperature prediction for permanent magnet synchronous motors,”
2017, doi: 10.1109/ijcnn.2017.7966088.'
mla: Wallscheid, Oliver, et al. “Investigation of Long Short-Term Memory Networks
to Temperature Prediction for Permanent Magnet Synchronous Motors.” 2017 International
Joint Conference on Neural Networks (IJCNN), 2017, doi:10.1109/ijcnn.2017.7966088.
short: 'O. Wallscheid, W. Kirchgässner, J. Böcker, in: 2017 International Joint
Conference on Neural Networks (IJCNN), 2017.'
date_created: 2021-02-16T21:21:51Z
date_updated: 2022-02-19T16:51:54Z
department:
- _id: '52'
doi: 10.1109/ijcnn.2017.7966088
language:
- iso: eng
publication: 2017 International Joint Conference on Neural Networks (IJCNN)
publication_identifier:
isbn:
- '9781509061822'
publication_status: published
status: public
title: Investigation of long short-term memory networks to temperature prediction
for permanent magnet synchronous motors
type: conference
user_id: '49265'
year: '2017'
...