| Permanent magnet synchronous motors have been widely used in our production due to their high power density,small size,light weight and many other advantages.With the development of high requirements for motors,more and more attention has been paid to their heat generation.Accurate prediction of motor temperature rise is very important for motor design,controller design and cooling system optimization design.Although the traditional thermal simulation analysis method can accurately simulate the temperature rise distribution of the motor,it is usually timeconsuming and requires high demand for users.The motor intensive parameter thermal network method can not only quickly predict the temperature rise of the motor,but also make the calculation results more accurate.Through in-depth analysis of the motor structure,this paper uses the knowledge of heat transfer to solve the equivalent thermal resistance of each part of the motor,builds a refined thermal model of the motor and a simplified thermal network model optimized and improved,and validates it through test conditions.Combined with the vehicle model to predict the temperature rise distribution of the various motor components of the vehicle under typical operating conditions,that is,the relationship between the vehicle driving conditions and the instantaneous heat generation of the motor is established.Finally,this paper analyzes the energy flow of the established vehicle model.The main research conclusions of this paper are as follows:(1)The motor’s refined thermal network model is tested and verified.Through the point-toend winding verification of the test conditions,it is found that the predicted temperature of the model followed the test temperature well,the maximum temperature difference does not exceed5℃,and the maximum error does not exceed 3.3%.The verification of the predicted temperature rise of the magnetic steel finds that the simulated temperature is consistent with the experimental temperature trend,and the maximum temperature difference does not exceed 7°C.The fine thermal model can predict the motor temperature rise very well.(2)The fine thermal model is combined with the vehicle model to predict the real-time average temperature distribution of the motor components under the CLTC-P and WLTC operating conditions of the vehicle,and the predicted temperature rise of the end windings under the same operating conditions is tested and verified.The temperature curve follows well,and the temperature difference between the two is basically below 5°C.Therefore,when the vehicle is running under cyclic test conditions,the thermal model can well predict the temperature rise distribution of each component of the motor.(3)The simplified thermal network model of the motor is tested and verified.The verification of the end windings finds that the predicted temperature of the model follows the test temperature well,and the maximum temperature difference is less than 5℃.The verification of the predicted temperature rise of the magnetic steel finds that the simulation temperature and the test temperature trend are consistent.The maximum temperature difference does not exceed 8℃,and the simplified thermal model can predict the temperature rise of the motor well.(4)The simplified thermal model is combined with the vehicle model to predict the real-time average temperature distribution of the motor components under the CLTC-P and WLTC operating conditions of the vehicle,and the predicted temperature rise of the end windings under the same operating conditions is tested and verified.The temperature curve follows well,and the temperature difference between the two is basically below 4°C.Therefore,when the vehicle is running under cyclic test conditions,the thermal model can well predict the temperature rise distribution of each component of the motor.(5)The energy flow distribution of the whole vehicle under different operating conditions of CLTC and WLTC was studied.Using the AMESim post-processing function,a dynamic graph of the energy consumption distribution and energy consumption proportion of each component of the vehicle is produced,and the energy consumption distribution and energy consumption proportion of the main components are analyzed,which is beneficial to the power matching and design optimization of the vehicle.(6)The changes of the vehicle powertrain efficiency,charging efficiency and power battery SOC with operating conditions and the influence of braking energy recovery on the power battery SOC are analyzed,which is helpful to further understand the vehicle’s power performance,economy,comfort and drivability,and provide guidance for the vehicle’s power matching and design optimization. |
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