{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:52:28Z","timestamp":1777704748457,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T00:00:00Z","timestamp":1592265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,7,17]]},"abstract":"<jats:p>\n                    The efficiency and control accuracy of Interior Permanent Magnet Synchronous Motor (IPMSM) are the main factors affecting performance. Manual calibration has the disadvantage of high work intensity, long calibration period and high technical requirement, which leads to low calibration accuracy and motor efficiency. Thus, a novel calibration method based on Deep Deterministic Policy Gradient (DDPG) and Long Short-Term Memory (LSTM) is proposed. By constructing a deep reinforcement learning network, the self-optimization of the optimal working point under any working condition is realized, and the MAP for IPMSM in full speed-torque range is obtained. The method can be used to quickly realize the optimal matching of\n                    <jats:italic>d-q<\/jats:italic>\n                    axis current with arbitrary stator current. It focuses on solving the problem of motor overheating caused by long adjustment time of manually calibrated MAP when the motor is overloaded, to realize fast calibration in overload area. Moreover, the method reduces the dependence on the motor parameters and increases the adaptability of the calibration MAP data to the operating conditions. The simulation and bench test indicate that the method can meet the response requirements of motor torque, and results reveal that the motor efficiency is greatly improved.\n                  <\/jats:p>","DOI":"10.3233\/jifs-191567","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T12:07:34Z","timestamp":1592568454000},"page":"607-626","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Bench calibration method for automotive electric motors based on deep reinforcement learning"],"prefix":"10.1177","volume":"39","author":[{"given":"Yafu","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hantao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linhui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,6,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2438772"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11082046"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2007.909087"},{"issue":"5","key":"e_1_3_2_5_2","first-page":"779","article-title":"Foc and dtc:Two viable schemes for induction motors torque control","volume":"17","author":"Casadei D.","year":"2004","unstructured":"CasadeiD., ProfumoF., SerraG. and TaniA., et al., Foc and dtc:Two viable schemes for induction motors torque control, Converter Technology & Electric Traction 17(5) (2004), 779\u2013787.","journal-title":"Converter Technology & Electric Traction"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2012.2207651"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2012.2227265"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2015.2417128"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2823538"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2017.2726980"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2784409"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2018.2877740"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/15325000902817218"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2013.2252024"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2015.2477469"},{"issue":"6","key":"e_1_3_2_16_2","first-page":"1708","article-title":"A robust dynamic decoupling control scheme for pmsm current loops based on improved sliding mode observer","volume":"18","author":"Shen H.L.","year":"2018","unstructured":"ShenH.L., LuoX., LiangG.L. and ShenA.W., A robust dynamic decoupling control scheme for pmsm current loops based on improved sliding mode observer, Journal of Power Electronics 18(6) (2018), 1708\u20131719.","journal-title":"Journal of Power Electronics"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2014.2354299"},{"issue":"4080","key":"e_1_3_2_18_2","first-page":"559","article-title":"Calibration system prototype for increasing thelevel of automation in stationary engine testing and calibration","volume":"1","author":"Ullmann S.","year":"2005","unstructured":"UllmannS., ReussH.-C. and ZellA., Calibration system prototype for increasing thelevel of automation in stationary engine testing and calibration, SAE TechnicalPaper 1(4080) (2005), 559\u2013560.","journal-title":"SAE TechnicalPaper"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","unstructured":"HuD. and XuL. Characterizing the torque lookup table of an ipm machine for automotive application (2014).","DOI":"10.1109\/ITEC-AP.2014.6940865"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2016.2528505"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2006.888781"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"ParkJ.W. KooD.H. KimJ.M. and KimH.G. Improvement of control characteristics of interior permanent-magnetsynchronous motor for electric vehicle 37(6) (2001) 1754\u20131760.","DOI":"10.1109\/28.968188"},{"key":"e_1_3_2_23_2","unstructured":"VafamandN. ArefiM.M. KhoobanM.H. and DragicevicT. et al. Nonlinear model predictive speed control of electric vehicles represented by linear parameter varying models with bias terms Ieee Journal of Emerging and Selected Topics in Power Electronics 1-1."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2003.821797"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2009.2018918"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.4037527"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"KhoobanM.H. GheisarnezhadM. VafamandN. and BoudjadarJ. Electric vehicle power propulsion system control based on time-varying fractional calculus: Implementation and experimental results IEEE Transactions on Intelligent Vehicles (2019).","DOI":"10.1109\/TIV.2019.2904415"},{"issue":"6","key":"e_1_3_2_28_2","first-page":"35","article-title":"Design novel fuzzy logic controller of ipmsm for electric vehicles","volume":"20","author":"Wu H.Y.","year":"2014","unstructured":"WuH.Y., WangS.H., ShaoK.R. and SunJ.B., Design novel fuzzy logic controller of ipmsm for electric vehicles, Elektronika Ir Elektrotechnika 20(6) (2014), 35\u201341.","journal-title":"Elektronika Ir Elektrotechnika"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2014.2317845"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2017.2665670"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"UddinM.N. and RebeiroR.S. Online efficiency optimization of a fuzzy logic controller based ipmsm drive Industry Applications Society Annual Meeting 2009. IAS 2009. IEEE (2009).","DOI":"10.1109\/IAS.2009.5324822"},{"key":"e_1_3_2_32_2","unstructured":"NavidV. KhoobanM.H. KhayatianA. and BlaabjergF. Design of robust double-fuzzy-summation non-pdc controller for chaotic power systems Journal of Dynamic Systems Measurement & Control"},{"key":"e_1_3_2_33_2","unstructured":"KhoobanM.H. VafamandN. and NiknamT. T\u2013s fuzzy model predictive speed control of electrical vehicles Isa Transactions S0019057816300659."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2351399"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2016.09.021"},{"key":"e_1_3_2_36_2","first-page":"90","volume-title":"An introduction to reinforcement learning","author":"Kaelbling L.P.","year":"1995","unstructured":"KaelblingL.P., LittmanM.L. and MooreA.W., An introduction to reinforcement learning, in: SteelsL. (Ed.) The Biology and Technology of Intelligent Autonomous Agents, Springer Berlin Heidelberg, Berlin, Heidelberg, (1995), pp. 90\u2013127."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2477810"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2292704"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2014.06.057"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"WangQ. YuH. WangM. and QiX. A novel adaptive neuro-control approach for permanent magnet synchronous motor speed control Energies (2018).","DOI":"10.3390\/en11092355"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2014.2334733"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2733494"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","unstructured":"KimS. YoonY.D. SulS.K. and IdeK. et al. Parameter independent maximum torque per ampere (mtpa) control of ipm machine based on signal injection Applied Power Electronics Conference & Exposition (2010).","DOI":"10.1109\/APEC.2010.5433685"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2010.2090842"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","unstructured":"DianovA. Young-KwanK. Sang-JoonL. and Sang-TaekL. Robust self-tuning mtpa algorithm for ipmsm drives Conference of the IEEE Industrial Electronics Society (2008).","DOI":"10.1109\/IECON.2008.4758151"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2012.2195203"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2009.2027640"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2019.2906649"},{"issue":"2","key":"e_1_3_2_49_2","first-page":"0","article-title":"Identification of machine parameters of a synchronous motor","volume":"41","author":"Rahman K.M.","unstructured":"RahmanK.M. and HitiS., Identification of machine parameters of a synchronous motor, Ieee Transactions on Industry Applications 41(2), 0\u2013565.","journal-title":"Ieee Transactions on Industry Applications"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2015.2477469"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.2018.2852219"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-191567","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-191567","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-191567","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:55Z","timestamp":1777455715000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-191567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,16]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,7,17]]}},"alternative-id":["10.3233\/JIFS-191567"],"URL":"https:\/\/doi.org\/10.3233\/jifs-191567","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,16]]}}}