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OLTEM: Lumped Thermal and Deep Neural Model for PMSM Temperature

dc.contributor.authorYuzhong Sheng
dc.contributor.authorXin Liu
dc.contributor.authorQi Chen
dc.contributor.authorZhenghao Zhu
dc.contributor.authorChuangxin Huang
dc.contributor.authorQiuliang Wang
dc.contributor.otherDepartment of Automation, University of Science and Technology of China, Hefei 230026, China
dc.contributor.otherGanjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341119, China
dc.contributor.otherGanjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341119, China
dc.contributor.otherGanjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341119, China
dc.contributor.otherGanjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341119, China
dc.contributor.otherDepartment of Automation, University of Science and Technology of China, Hefei 230026, China
dc.date.accessioned2025-08-27T14:03:35Z
dc.date.accessioned2025-10-08T08:52:45Z
dc.date.available2025-10-08T08:52:45Z
dc.date.issued01-07-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/37879
dc.description.abstract<b>Background and Objective:</b> Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. <b>Methods:</b> OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. <b>Results:</b> OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C<sup>2</sup> and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. <b>Conclusions:</b> By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccElectronic computers. Computer science
dc.titleOLTEM: Lumped Thermal and Deep Neural Model for PMSM Temperature
dc.typeArticle
dc.description.keywordsattention mechanism
dc.description.keywordslumped parameter thermal network
dc.description.keywordspower loss estimation
dc.description.keywordstemperature prediction
dc.description.doi10.3390/ai6080173
dc.title.journalAI
dc.identifier.e-issn2673-2688
dc.identifier.oaioai:doaj.org/journal:5408e08a30bb4c3ba3f7faad77b33ca3
dc.journal.infoVolume 6, Issue 8


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