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Kirchgässner, Wilhelm, et al. “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations.”10.23919/ipec-himeji2022-ecce53331.2022.9807209__2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, 2022, doi:10.23919/ipec-himeji2022-ecce53331.2022.9807209.
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Kirchgässner, Wilhelm, et al. “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark.”10.1109/tec.2021.3052546__IEEE Transactions on Energy Conversion, vol. 36, no. 3, 2021, pp. 2059–67, doi:10.1109/tec.2021.3052546.
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Kirchgässner, Wilhelm, et al. “Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning.” ArXiv Preprint ArXiv:2103.16323, 2021.
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Kirchgässner, Wilhelm, et al. “Estimating Electric Motor Temperatures with Deep Residual Machine Learning.”10.1109/tpel.2020.3045596__IEEE Transactions on Power Electronics, vol. 36, no. 7, 2020, pp. 7480–88, doi:10.1109/tpel.2020.3045596.
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Schenke, Maximilian, et al. “Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept.”10.1109/tii.2019.2948387__IEEE Transactions on Industrial Informatics, 2019, pp. 4650–58, doi:10.1109/tii.2019.2948387.
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Kirchgässner, Wilhelm, et al. “Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines.”10.1109/isie.2019.8781195__2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019, doi:10.1109/isie.2019.8781195.
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Kirchgässner, Wilhelm, et al. “Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors.”10.1109/iemdc.2019.8785109__2019 IEEE International Electric Machines &; Drives Conference (IEMDC), 2019, doi:10.1109/iemdc.2019.8785109.
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