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
LibreCat: /record/34065
DOI: 10.1016/j.engappai.2022.105537

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
LibreCat: /record/32859
DOI: 10.23919/ipec-himeji2022-ecce53331.2022.9807209

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.
LibreCat: /record/42894

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
LibreCat: /record/22162
DOI: 10.1109/ojpel.2021.3065877

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
LibreCat: /record/21251
DOI: 10.1109/tec.2021.3052546

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
LibreCat: /record/21254
DOI: 10.21105/joss.02498

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.
LibreCat: /record/29655

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
LibreCat: /record/21252
DOI: 10.1109/tnnls.2020.3029573

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
LibreCat: /record/21250
DOI: 10.1109/tpel.2020.3045596

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.
LibreCat: /record/29640

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
LibreCat: /record/25030
DOI: 10.1109/tii.2019.2948387

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
LibreCat: /record/21247
DOI: 10.1109/isie.2019.8781195

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
LibreCat: /record/21249
DOI: 10.1109/iemdc.2019.8785109

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
LibreCat: /record/21248
DOI: 10.1109/ijcnn.2017.7966088