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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 

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 

Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (2022). LLC Resonant Converter Modulations for Reduced Junction Temperatures in Half-Bridge Mode and Transformer Flux in the On-the-Fly Morphing Thereto. IEEE Transactions on Power Electronics, 37(11), 13413–13427.https://doi.org/10.1109/tpel.2022.3180758 

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.

Brosch, A., Rauhaus, J., Wallscheid, O., Böcker, J., &; Zimmer, D. (2022). Data-Driven Adaptive Torque Oscillation Compensation for Multi-Motor Drive Systems. IEEE Open Journal of Industry Applications.https://doi.org/10.1109/ojia.2022.3171333 

Förster, N., Hölscher, J., Piepenbrock, T., Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (2022). An Open-Source FEM Magnetic Toolbox for Calculating Electric and Thermal Behavior of Power Electronic Magnetic Components. 2022 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), P.1-P.9.

Brosch, A., Wallscheid, O., &; Böcker, J. (2021). Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation. IEEE Open Journal of Industry Applications, 2, 47–63.https://doi.org/10.1109/OJIA.2021.3066105 

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

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 

Weber, D., Heid, S., Bode, H., Lange, J., Hüllermeier, E., &; Wallscheid, O. (2021). Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. IEEE Access, 9, 35654–35669.https://doi.org/10.1109/ACCESS.2021.3062144 

Stender, M., Wallscheid, O., &; Böcker, J. (2021). Accurate Torque Control for Induction Motors by Utilizing a Globally Optimized Flux Observer. IEEE Transactions on Power Electronics, 36(11), 13261–13274.https://doi.org/10.1109/tpel.2021.3080129 

Stender, M., Wallscheid, O., &; Böcker, J. (2021). Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach. 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC).https://doi.org/10.1109/pemc48073.2021.9432615 

Stender, M., Wallscheid, O., &; Böcker, J. (2021). Combined Electrical-Thermal Gray-Box Model and Parameter Identification of an Induction Motor. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society.https://doi.org/10.1109/iecon48115.2021.9589225 

Stender, M., Wallscheid, O., &; Böcker, J. (2021). Gray-Box Loss Model for Induction Motor Drives. 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC).https://doi.org/10.1109/pemc48073.2021.9432491 

Hanke, S., Wallscheid, O., &; Böcker, J. (2021). Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor.https://doi.org/10.1049%2Ficp.2021.1122 

Wallscheid, O. (2021). Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges. IEEE Open Journal of Industry Applications.

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 

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 

Schenke, M., &; Wallscheid, O. (2021). A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors. IEEE Open Journal of the Industrial Electronics Society, 388–400.https://doi.org/10.1109/ojies.2021.3075521