A. Brosch, O. Wallscheid, and J. Böcker, “Long-Term Memory Recursive Least Squares Online Identification of Highly Utilized Permanent Magnet Synchronous Motors for Finite-Control-Set Model Predictive Control,”10.1109/tpel.2022.3206598__IEEE Transactions on Power Electronics, 2022, doi:10.1109/tpel.2022.3206598 .

J. Lange, D. Schmies, K. S. C. Stille, J. Böcker, and O. Wallscheid, “Experimental Comparison of FPGA-Implemented Model Predictive Voltage Control to Cascaded Proportional Resonant Control for a Three-Phase Four-Wire Three-Level Grid-Forming Inverter of 250 kVA,” presented at the EPE’22 ECCE Europe, Hannover, 2022.

M. Stender, M. Becker, O. Wallscheid, and J. Böcker, “Adaptive Operating Strategy for Induction Motors Under Changing Electrical-Thermal Conditions,”10.1109/IECON49645.2022.9968695__presented at the 48th Annual Conference of the Industrial Electronics Society IECON 2022 Conference, Brüssel, 2022, doi:10.1109/IECON49645.2022.9968695 .

N. Förster, P. Rehlaender, O. Wallscheid, F. Schafmeister, and J. Böcker, “An Open-Source Transistor Database and Toolbox as a Unified Software Engineering Tool for Managing and Evaluating Power Transistors,”10.1109/apec43599.2022.9773701__2022, doi:10.1109/apec43599.2022.9773701 .

N. Förster, T. Piepenbrock, P. Rehlaender, O. Wallscheid, F. Schafmeister, and J. Böcker, “An Open-Source FEM Magnetics Toolbox for Power Electronic Magnetic Components,”10.30420/565822103__in PCIM Europe 2022; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, 2022, pp. 1–10, doi:10.30420/565822103 .

B. Haucke-Korber, M. Schenke, and O. Wallscheid, “Reinforcement Learning-Based Deep Q Direct Torque Control with Adaptable Switching Frequency Towards Six-Step Operation of Permanent Magnet Synchronous Motors,” in IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning,”10.1016/j.engappai.2022.105537__Engineering Applications of Artificial Intelligence, vol. 117, Art. no. 105537, 2022, doi:10.1016/j.engappai.2022.105537 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations,”10.23919/ipec-himeji2022-ecce53331.2022.9807209__2022, doi:10.23919/ipec-himeji2022-ecce53331.2022.9807209 .

P. Rehlaender, O. Wallscheid, F. Schafmeister, and J. Böcker, “LLC Resonant Converter Modulations for Reduced Junction Temperatures in Half-Bridge Mode and Transformer Flux in the On-the-Fly Morphing Thereto,”10.1109/tpel.2022.3180758__IEEE Transactions on Power Electronics, vol. 37, no. 11, pp. 13413–13427, 2022, doi:10.1109/tpel.2022.3180758 .

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.

A. Brosch, J. Rauhaus, O. Wallscheid, J. Böcker, and D. Zimmer, “Data-Driven Adaptive Torque Oscillation Compensation for Multi-Motor Drive Systems,”10.1109/ojia.2022.3171333__IEEE Open Journal of Industry Applications, 2022, doi:10.1109/ojia.2022.3171333 .

N. Förster et al., “An Open-Source FEM Magnetic Toolbox for Calculating Electric and Thermal Behavior of Power Electronic Magnetic Components,” in 2022 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), 2022, p. P.1-P.9.

A. Brosch, O. Wallscheid, and J. Böcker, “Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation,”10.1109/OJIA.2021.3066105__IEEE Open Journal of Industry Applications, vol. 2, pp. 47–63, 2021, doi:10.1109/OJIA.2021.3066105 .

E. G. Gedlu, O. Wallscheid, and J. Böcker, “Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks,”10.1109/IEMDC47953.2021.9449548__in 2021 IEEE International Electric Machines &; Drives Conference (IEMDC), 2021, pp. 1–8, doi:10.1109/IEMDC47953.2021.9449548 .

G. Book et al., “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments,”10.1109/ojpel.2021.3065877__IEEE Open Journal of Power Electronics, pp. 187–201, 2021, doi:10.1109/ojpel.2021.3065877 .

D. Weber, S. Heid, H. Bode, J. Lange, E. Hüllermeier, and O. Wallscheid, “Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments,”10.1109/ACCESS.2021.3062144__IEEE Access, vol. 9, pp. 35654–35669, 2021, doi:10.1109/ACCESS.2021.3062144 .

M. Stender, O. Wallscheid, and J. Böcker, “Accurate Torque Control for Induction Motors by Utilizing a Globally Optimized Flux Observer,”10.1109/tpel.2021.3080129__IEEE Transactions on Power Electronics, vol. 36, no. 11, pp. 13261–13274, 2021, doi:10.1109/tpel.2021.3080129 .

M. Stender, O. Wallscheid, and J. Böcker, “Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach,”10.1109/pemc48073.2021.9432615__2021, doi:10.1109/pemc48073.2021.9432615 .

M. Stender, O. Wallscheid, and J. Böcker, “Combined Electrical-Thermal Gray-Box Model and Parameter Identification of an Induction Motor,”10.1109/iecon48115.2021.9589225__2021, doi:10.1109/iecon48115.2021.9589225 .

M. Stender, O. Wallscheid, and J. Böcker, “Gray-Box Loss Model for Induction Motor Drives,”10.1109/pemc48073.2021.9432491__2021, doi:10.1109/pemc48073.2021.9432491 .