Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Thermal Neural Networks: Lumped-Parameter Thermal Modeling with State-Space Machine Learning.”https://doi.org/10.1016/j.engappai.2022.105537__Engineering Applications of Artificial Intelligence 117 (2022).https://doi.org/10.1016/j.engappai.2022.105537 .

Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations.”https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209__In 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia). IEEE, 2022.https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209 .

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.

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.”https://doi.org/10.1109/ojpel.2021.3065877__IEEE Open Journal of Power Electronics, 2021, 187–201.https://doi.org/10.1109/ojpel.2021.3065877 .

Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark.”https://doi.org/10.1109/tec.2021.3052546__IEEE Transactions on Energy Conversion 36, no. 3 (2021): 2059–67.https://doi.org/10.1109/tec.2021.3052546 .

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.”https://doi.org/10.21105/joss.02498__Journal of Open Source Software, 2021.https://doi.org/10.21105/joss.02498 .

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.

Traue, Arne, Gerrit Book, Wilhelm Kirchgässner, and Oliver Wallscheid. “Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control.”https://doi.org/10.1109/tnnls.2020.3029573__IEEE Transactions on Neural Networks and Learning Systems, 2020, 1–10.https://doi.org/10.1109/tnnls.2020.3029573 .

Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Estimating Electric Motor Temperatures with Deep Residual Machine Learning.”https://doi.org/10.1109/tpel.2020.3045596__IEEE Transactions on Power Electronics 36, no. 7 (2020): 7480–88.https://doi.org/10.1109/tpel.2020.3045596 .

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.

Schenke, Maximilian, Wilhelm Kirchgässner, and Oliver Wallscheid. “Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept.”https://doi.org/10.1109/tii.2019.2948387__IEEE Transactions on Industrial Informatics, 2019, 4650–58.https://doi.org/10.1109/tii.2019.2948387 .

Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines.”https://doi.org/10.1109/isie.2019.8781195__In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019.https://doi.org/10.1109/isie.2019.8781195 .

Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors.”https://doi.org/10.1109/iemdc.2019.8785109__In 2019 IEEE International Electric Machines &; Drives Conference (IEMDC), 2019.https://doi.org/10.1109/iemdc.2019.8785109 .

Wallscheid, Oliver, Wilhelm Kirchgässner, and Joachim Böcker. “Investigation of Long Short-Term Memory Networks to Temperature Prediction for Permanent Magnet Synchronous Motors.”https://doi.org/10.1109/ijcnn.2017.7966088__In 2017 International Joint Conference on Neural Networks (IJCNN), 2017.https://doi.org/10.1109/ijcnn.2017.7966088 .