Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy
The actual performance of driving motors in the electric vehicle (EV) powertrain depends not only on the electromagnetic design of the motor itself but also on the driving condition of the vehicle. The traditional motor optimization method at the rated point is difficult to deal with because of the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/19/10792 |
| _version_ | 1797576166469533696 |
|---|---|
| author | Jiabao Bu Shangbin Yuan Jinhua Du |
| author_facet | Jiabao Bu Shangbin Yuan Jinhua Du |
| author_sort | Jiabao Bu |
| collection | DOAJ |
| description | The actual performance of driving motors in the electric vehicle (EV) powertrain depends not only on the electromagnetic design of the motor itself but also on the driving condition of the vehicle. The traditional motor optimization method at the rated point is difficult to deal with because of the mismatch between its high-efficiency area and the actual operation area. This paper systematically proposes an optimal design method for driving motors for EVs, considering the driving conditions and control strategy to improve motor efficiency and passengers’ riding comfort. It uses cluster analysis to identify representative points and related energy weights to consider motors’ comprehensive performance in different driving cycles. Three typical operation conditions are selected to implement the proposed optimization process. In the design process, by using the sensitivity analysis method, the significance of the structural parameters is effectively evaluated. Moreover, the semianalytical efficiency model and torque model of permanent magnet driving motors based on finite element analysis results are deduced to consider the influence of magnetic saturation, space harmonics, and cross-coupling between d-axis and q-axis magnetic fields. Based on the driving system demands of an A0 class pure EV, the whole optimization design is divided into four steps and three scales, including the motor scale, control scale, and system scale. By using the multi-objective optimization method, Pareto optimality of motor efficiency and torque ripple is achieved under the city driving cycle and highway driving cycle. Compared to the optimization only at the rated condition, the proportion of motor sweet region increased about 1.25 times and 3.5 times by the proposed system-scale optimization under two driving cycles, respectively. Finally, the effectiveness of the proposed optimization method is verified by the prototype experiments. |
| first_indexed | 2024-03-10T21:49:21Z |
| format | Article |
| id | doaj.art-2f8d88c27e2d4ba088f24adc9fabcb69 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:49:21Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2f8d88c27e2d4ba088f24adc9fabcb692023-11-19T14:04:16ZengMDPI AGApplied Sciences2076-34172023-09-0113191079210.3390/app131910792Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control StrategyJiabao Bu0Shangbin Yuan1Jinhua Du2The State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaThe State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaThe State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaThe actual performance of driving motors in the electric vehicle (EV) powertrain depends not only on the electromagnetic design of the motor itself but also on the driving condition of the vehicle. The traditional motor optimization method at the rated point is difficult to deal with because of the mismatch between its high-efficiency area and the actual operation area. This paper systematically proposes an optimal design method for driving motors for EVs, considering the driving conditions and control strategy to improve motor efficiency and passengers’ riding comfort. It uses cluster analysis to identify representative points and related energy weights to consider motors’ comprehensive performance in different driving cycles. Three typical operation conditions are selected to implement the proposed optimization process. In the design process, by using the sensitivity analysis method, the significance of the structural parameters is effectively evaluated. Moreover, the semianalytical efficiency model and torque model of permanent magnet driving motors based on finite element analysis results are deduced to consider the influence of magnetic saturation, space harmonics, and cross-coupling between d-axis and q-axis magnetic fields. Based on the driving system demands of an A0 class pure EV, the whole optimization design is divided into four steps and three scales, including the motor scale, control scale, and system scale. By using the multi-objective optimization method, Pareto optimality of motor efficiency and torque ripple is achieved under the city driving cycle and highway driving cycle. Compared to the optimization only at the rated condition, the proportion of motor sweet region increased about 1.25 times and 3.5 times by the proposed system-scale optimization under two driving cycles, respectively. Finally, the effectiveness of the proposed optimization method is verified by the prototype experiments.https://www.mdpi.com/2076-3417/13/19/10792multi-objective optimizationcontrol strategydriving cycleelectric vehicle |
| spellingShingle | Jiabao Bu Shangbin Yuan Jinhua Du Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy Applied Sciences multi-objective optimization control strategy driving cycle electric vehicle |
| title | Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy |
| title_full | Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy |
| title_fullStr | Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy |
| title_full_unstemmed | Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy |
| title_short | Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy |
| title_sort | clustering optimization of ipmsm for electric vehicles considering inverter control strategy |
| topic | multi-objective optimization control strategy driving cycle electric vehicle |
| url | https://www.mdpi.com/2076-3417/13/19/10792 |
| work_keys_str_mv | AT jiabaobu clusteringoptimizationofipmsmforelectricvehiclesconsideringinvertercontrolstrategy AT shangbinyuan clusteringoptimizationofipmsmforelectricvehiclesconsideringinvertercontrolstrategy AT jinhuadu clusteringoptimizationofipmsmforelectricvehiclesconsideringinvertercontrolstrategy |