新能源汽車驅(qū)動(dòng)電機(jī)冷卻系統(tǒng)劣化故障預(yù)測

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Deterioration fault prediction of the drive-motor cooling-system fornewenergyvehicles

LIU Chiwei1， HUANG Yundi2

1.InstituteofMechanicalandElectricalEngineering，ZhongshanPolytechnic，Zhongshan528404，China； 2.InstituteofInformationEngineering，ZhongshanPolytechnic，Zhongshan528404，China）

Abstract：Amulti-clasifiermodel of Principal-Component-Analysisand theParticle-Swarm-Optimization Support-Vector-Machine （PCA-GOA-LSSVM）was proposed todetectand predict thedeteriorationofthe cooling systemofthe drive motorof new energy vehiclesas earlyas possbleand reduce the occurrence of motor power limit orshutdowncaused byexcessivecoolant temperature.The Principal Component Analysis （PCA）method was used to reduce the dimensionalityand reconstruct the fault characteristics.The Grasshopper Optimization Algorithm （GOA）was used to optimize parameters of Least Square Support Vector Machine （LSSVM）.The sampledatacollcted from thereal vehicle fault test，were respectivelyinputto the LSSVM prediction model， （PCA-PSO-SVM），andthe PCA-GOA-LSSVM models forcomparison testing.The results show that for the multi-classification prediction model based on PCA-GOA-LSSVM，the accuracy reaches 91.41% with a precision of 86.25% ，which is higher than the compared prediction model.The model can be used in the performance deteriorationpredictionandfaultdiagnosisofthecoolingsystemofthedrivemotorof newenergyvehicles，and canaccuratelyremind tomaintain thevehicle timelyand effectively judge the fault type.

Keywords：new energyvehicles；drive-motorcooling-system；fault prediction；least squares support vector machine （LSSVM）； grasshopper optimization algorithm （GOA）； principal component analysis （PCA）

開展驅(qū)動(dòng)電機(jī)冷卻系統(tǒng)故障預(yù)測研究，實(shí)時(shí)監(jiān)控冷卻系統(tǒng)散熱性能的劣化情況，實(shí)現(xiàn)早期故障預(yù)警，及時(shí)進(jìn)行相關(guān)裝置的檢查維護(hù)，可有效減少車輛故障機(jī)率，優(yōu)化驅(qū)動(dòng)電機(jī)冷卻系統(tǒng)產(chǎn)品開發(fā)和設(shè)計(jì)。（剩余12629字）