{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:05:07Z","timestamp":1761491107642,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"YuLai Zhang","award":["Nsfc61803337"],"award-info":[{"award-number":["Nsfc61803337"]}]},{"name":"Cheng Zhao","award":["61902349"],"award-info":[{"award-number":["61902349"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurate temperature prediction plays an important role in the thermal protection of permanent magnet synchronous motors. A temperature prediction method of permanent magnet synchronous machines (PMSMs) based on proximal policy optimization is proposed. In the proposed method, the actor-critic framework of reinforcement learning is introduced to model the effective temperature prediction mechanism, and the correlations between the input features are then analyzed to select the appropriate input features. Finally, the simplified proximal policy optimization algorithm is introduced to optimize the value of the prediction temperature of PMSMs. Experimental results reveal the high accuracy and reliability of the proposed method compared with an exponential weighted moving average method (EWMA), a recurrent neural network (RNN), and long short-term memory (LSTM).<\/jats:p>","DOI":"10.3390\/info11110495","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T08:59:28Z","timestamp":1603443568000},"page":"495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization"],"prefix":"10.3390","volume":"11","author":[{"given":"Yuefeng","family":"Cen","sequence":"first","affiliation":[{"name":"School of Information and Electronic Engineering, ZheJiang University of Seience and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenguang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, ZheJiang University of Seience and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Cen","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, ZheJiang University of Seience and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, ZheJiang University of Seience and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-4222","authenticated-orcid":false,"given":"Cheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Economics, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,23]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xiao, M., Lu, K., Wu, Z.H., and Tao, B. (2019). A Simplified Thermal Model and Online Temperature Estimation Method of Permanent Magnet Synchronous Motors. Appl. Sci., 9.","key":"ref_1","DOI":"10.3390\/app9153158"},{"key":"ref_2","first-page":"276","article-title":"Temperature Prediction and Thermal Boundary Simulation Using Hardware-in-Loop Method for Permanent Magnet Synchronous Motors","volume":"21","author":"Li","year":"2016","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1109\/TPEL.2013.2253128","article-title":"A Practical Thermal Model for the Estimation of Permanent Magnet and Stator Winding Temperatures","volume":"29","author":"Kral","year":"2014","journal-title":"IEEE Trans. Power Electron."},{"doi-asserted-by":"crossref","unstructured":"Mohamed, A.H., Hemeida, A., Rashekh, A., Vansompel, H., Arkkio, A., and Sergeant, P. (2018). A 3D Dynamic Lumped Parameter Thermal Network of Air-Cooled YASA Axial Flux Permanent Magnet Synchronous Machine. Energies, 11.","key":"ref_4","DOI":"10.3390\/en11040774"},{"unstructured":"Wallscheid, O., Specht, A., and B\u00f6ecker, J. (2014, January 18\u201321). Determination of Rotor Temperature for an Interior Permanent Magnet Synchronous Machine Using a Precise Flux Observer. Proceedings of the 2014 International Power Electronics Conference (IPEC-Hiroshima 2014\u2014ECCE ASIA), Hiroshima, Japan.","key":"ref_5"},{"doi-asserted-by":"crossref","unstructured":"Wallscheid, O., Kirchg\u00e4essner, W., and B\u00f6ecker, J. (2017, January 14\u201319). Investigation of Long Short-Term Memory Networks to Temperature Prediction for Permanent Magnet Synchronous Motors. Proceedings of the 2017 International Joint Conference On Neural Networks (IJCNN), Anchorage, AK, USA.","key":"ref_6","DOI":"10.1109\/IJCNN.2017.7966088"},{"key":"ref_7","first-page":"13","article-title":"Thermal Analysis of PMSM Based on Lumped Parameter Thermal Network Method","volume":"12","author":"Lan","year":"2017","journal-title":"J. Electr. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6116","DOI":"10.1109\/TIE.2017.2682010","article-title":"Analytical Thermal Model for Fast Stator Winding Temperature Prediction","volume":"64","author":"Sciascera","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","first-page":"18","article-title":"Signal injection strategy optimization of stator winding temperature estimation for permanent magnet synchronous motor","volume":"23","author":"Liu","year":"2019","journal-title":"Electr. Mach. Control"},{"key":"ref_10","first-page":"34","article-title":"Research on Temperature Field of the Permanent Magnet Synchronous Motors for Hybrid Vehicles Cooled by Oil","volume":"4","author":"Du","year":"2019","journal-title":"Automob. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.buildenv.2018.10.062","article-title":"Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method","volume":"148","author":"Xu","year":"2019","journal-title":"Build. Environ."},{"doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, T., Han, G.J., and Gou, Y. (2018). TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction. Sensors, 18.","key":"ref_12","DOI":"10.3390\/s18113797"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3921","DOI":"10.1109\/TIE.2017.2652363","article-title":"Observing the Permanent-Magnet Temperature of Synchronous Motors Based on Electrical Fundamental Wave Model Quantities","volume":"64","author":"Wallscheid","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal Policy Optimization algorithms. arXiv.","key":"ref_14"},{"unstructured":"Berthold, M., and H\u00f6ppner, F. (2016). On Clustering Time Series Using Euclidean Distance and Pearson Correlation. arXiv.","key":"ref_15"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1080\/00031305.2016.1154108","article-title":"The ASA\u2019s Statement on p-values: Context, Process, and Purpose","volume":"70","author":"Wasserstein","year":"2016","journal-title":"Am. Stat."},{"unstructured":"Wang, Z.Y., Bapst, V., Heess, N., Mnih, V., Munos, R., Kavukcuoglu, K., and Freitas, N.D. (2016). Sample Efficient Actor-Critic with Experience Replay. arXiv.","key":"ref_17"},{"doi-asserted-by":"crossref","unstructured":"Hietaharju, P., Ruusunen, M., and Leivisk\u00e4, K. (2018). A Dynamic Model for Indoor Temperature Prediction in Buildings. Energies, 11.","key":"ref_18","DOI":"10.3390\/en11061477"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.future.2020.02.022","article-title":"Smart data driven quality prediction for urban water source management","volume":"107","author":"Wu","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D. (2017). Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. arXiv.","key":"ref_20","DOI":"10.1145\/3128572.3140444"},{"doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D. (2016). Towards Evaluating the Robustness of Neural Networks. arXiv.","key":"ref_21","DOI":"10.1109\/SP.2017.49"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/11\/495\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:26:42Z","timestamp":1760178402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/11\/495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,23]]},"references-count":21,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["info11110495"],"URL":"https:\/\/doi.org\/10.3390\/info11110495","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2020,10,23]]}}}