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 
LibreCat: /record/29653
DOI: 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 
LibreCat: /record/30030
DOI: 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 
LibreCat: /record/30031
DOI: 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 
LibreCat: /record/30029
DOI: 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 
LibreCat: /record/30032
DOI: 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 
LibreCat: /record/29665
DOI: 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.
LibreCat: /record/29664

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 
LibreCat: /record/21251
DOI: 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 
LibreCat: /record/21254
DOI: 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 
LibreCat: /record/25031
DOI: 10.1109/ojies.2021.3075521

Schenke, M., &; Wallscheid, O. (2021). Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning. ArXiv Preprint ArXiv:2105.08990.
LibreCat: /record/29662

Brosch, A., Wallscheid, O., &; Böcker, J. (2021). Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors. IEEE Transactions on Industrial Informatics.https://doi.org/10.1109/tii.2021.3060469 
LibreCat: /record/21557
DOI: 10.1109/tii.2021.3060469

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

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

Kirchgässner, W., Wallscheid, O., &; Böcker, J. (2020). Estimating Electric Motor Temperatures with Deep Residual Machine Learning. IEEE Transactions on Power Electronics, 36(7), 7480–7488.https://doi.org/10.1109/tpel.2020.3045596 
LibreCat: /record/21250
DOI: 10.1109/tpel.2020.3045596

Brosch, A., Hanke, S., Wallscheid, O., &; Böcker, J. (2020). Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors. IEEE Transactions on Power Electronics, 2179–2190.https://doi.org/10.1109/tpel.2020.3006779 
LibreCat: /record/21558
DOI: 10.1109/tpel.2020.3006779

Stille, K. S. C., Weber, D., Lange, J., Vogt, T., Wallscheid, O., &; Böcker, J. (2020). Emulation of Microgrids for Research and Validation of Control and Operation Strategies. 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). SPEEDAM, Sorrent, Italy.https://doi.org/10.1109/speedam48782.2020.9161971 
LibreCat: /record/29956
DOI: 10.1109/speedam48782.2020.9161971

Stender, M., Wallscheid, O., &; Böcker, J. (2020). Comparison of Gray-Box and Black-Box Two-Level Three-Phase Inverter Models for Electrical Drives. IEEE Transactions on Industrial Electronics, 68(9), 8646–8656.https://doi.org/10.1109/tie.2020.3018060 
LibreCat: /record/30033
DOI: 10.1109/tie.2020.3018060

Stender, M., Wallscheid, O., &; Böcker, J. (2020). Accurate Torque Estimation for Induction Motors by Utilizing Globally Optimized Flux Observers. 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM).https://doi.org/10.1109/speedam48782.2020.9161955 
LibreCat: /record/30036
DOI: 10.1109/speedam48782.2020.9161955

Heid, S., Weber, D., Bode, H., Hüllermeier, E., &; Wallscheid, O. (2020). OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control. Journal of Open Source Software, 5(54), 2435.
LibreCat: /record/29649