Optimization of Permanent Magnet Machine for Electric Vehicles Considering the Control Strategy and Inverter

Yuan, Shangbin; Du, Jun; Yang, Wei

doi:10.23919/icems50442.2020.9291174

Cited by 1 publication

(5 citation statements)

References 14 publications

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“…The target driving system that powers an A0 class pure EV [34] consists of a 48-slot 8-pole IPMSM and a full-speed domain vector controller with MTPA and flux weakening control strategies, whose design requirements are shown in Table 1.…”

Section: Clustering Optimization Design Proceduresmentioning

confidence: 99%

“…The multi-objective PSO algorithm is utilized to obtain the optimal design, which includes adaptive mesh generation based on the roulette algorithm and global best updating as presented in [34].…”

Section: Optimization Algorithm and Fitness Functionmentioning

confidence: 99%

Section: Optimization Model For Motor Levelmentioning

confidence: 99%

“…Therefore, x = [x1, x2, x3, x4] T . It should be noted that the outer and inner diameters and the stack length are fixed [34] throughout the optimization process to meet the maximum installation space shown in Table 3. For each RP with a specific torque and speed, motor performance is evaluated by semianalytical models based on FEA results to balance accuracy and computation cost.…”

Section: Optimization Model For Motor Levelmentioning

confidence: 99%

“…To deal with the aforementioned issue, this paper presents an effort to develop a system-scale multi-objective optimization procedure for driving motors based on the clustering technique under different operating conditions. And a full-speed domain vector controller with maximum torque per current (MTPA) and flux weakening control strategies [32][33][34] is adopted to meet the requirements of EVs. The main contributions of this paper are shown as follows:…”

Section: Introductionmentioning

confidence: 99%

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Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy

Bu,

Yuan,

2023

Applied Sciences

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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.

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Section: Clustering Optimization Design Proceduresmentioning

confidence: 99%

Section: Optimization Algorithm and Fitness Functionmentioning

confidence: 99%

Section: Optimization Model For Motor Levelmentioning

confidence: 99%

Section: Optimization Model For Motor Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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