Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning
W. Kirchgässner, O. Wallscheid, J. Böcker, Engineering Applications of Artificial Intelligence 117 (2022).
LibreCat: /record/34065
DOI: 10.1016/j.engappai.2022.105537

Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations
W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, 2022.
LibreCat: /record/32859
DOI: 10.23919/ipec-himeji2022-ecce53331.2022.9807209

Application of Thermal Neural Networks on a Small-Scale Electric Motor
W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, J. Böcker, in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.
LibreCat: /record/42894

Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments
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.
LibreCat: /record/22162
DOI: 10.1109/ojpel.2021.3065877

Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark
W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Energy Conversion 36 (2021) 2059–2067.
LibreCat: /record/21251
DOI: 10.1109/tec.2021.3052546

gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems
P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, O. Wallscheid, Journal of Open Source Software (2021).
LibreCat: /record/21254
DOI: 10.21105/joss.02498

Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
W. Kirchgässner, O. Wallscheid, J. Böcker, ArXiv Preprint ArXiv:2103.16323 (2021).
LibreCat: /record/29655

Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control
A. Traue, G. Book, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Neural Networks and Learning Systems (2020) 1–10.
LibreCat: /record/21252
DOI: 10.1109/tnnls.2020.3029573

Estimating Electric Motor Temperatures with Deep Residual Machine Learning
W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics 36 (2020) 7480–7488.
LibreCat: /record/21250
DOI: 10.1109/tpel.2020.3045596

Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning
W. Kirchgässner, O. Wallscheid, J. Böcker, ArXiv Preprint ArXiv:2001.06246 (2020).
LibreCat: /record/29640

Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept
M. Schenke, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Industrial Informatics (2019) 4650–4658.
LibreCat: /record/25030
DOI: 10.1109/tii.2019.2948387

Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines
W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019.
LibreCat: /record/21247
DOI: 10.1109/isie.2019.8781195

Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors
W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE International Electric Machines &; Drives Conference (IEMDC), 2019.
LibreCat: /record/21249
DOI: 10.1109/iemdc.2019.8785109

Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors
O. Wallscheid, W. Kirchgässner, J. Böcker, in: 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
LibreCat: /record/21248
DOI: 10.1109/ijcnn.2017.7966088