Abstract：Aiming at the multiple operating conditions of the low speed and high torque motor drive system for unmanned delivery vehicles, combined with practical goals such as reducing manufacturing costs and increasing magnetic gathering capacity, a consequent pole unequal Halbach vernier wheel motor (CPUHVWM) is adopted to meet the performance requirements. However, there are problems such as torque ripple, high cost, and high loss that require optimization.The optimization methods can be divided into two main types.The first method is based on finite element method (FEM), which has low computational efficiency and focuses on single-objective optimization, making it difficult to find a global optimal solution. The other is algorithm optimization, which mainly establishes the relationship between structural parameters and objective functions and performs optimization processing through algorithms. The large number of operating points makes it challenging for single point based optimization techniques to fully satisfy vehicle performance requirements. This paper introduces an optimization framework that is predicated upon comprehensive operational conditions.The optimization goals center around reducing the motor costs, minimizing torque ripple, and enhancing efficiency, all while accounting for potential permanent magnet demagnetization and limitations in DC bus voltage. Due to the large number of motor driving operating points, the motor operation stages are divided into frequent start and stop, low speed climbing, low speed cruising, medium speed cruising, and high speed cruising.Utilizing K-means clustering analysis, the centroid of each data cluster is ascertained, and the weight of the region is assigned, thereby ensuring that each operating point is duly considered in the subsequent optimization design.After that, combined with redundancy design, the performance parameters required for the final motor were selected, and the operating principle of the motor and the selection of pole slot ratio were analyzed. To ensure the effectiveness of subsequent motor parameter optimization, the current distribution scheme within the voltage and current limit circles and the demagnetization of the PMs are analyzed, respectively. It can be seen that in this current distribution scheme, the impact on the PMs is not significant, and it is in the reversible demagnetization region. Therefore, in subsequent optimization, only the limitation of bus voltage on output performance needs to be considered. The motor parameters are divided into high-sensitivity regions and low-sensitivity regions. Only the high-sensitivity parameters are modeled using regression models, and a polynomial function is constructed. Subsequently, non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) is adopted to simultaneously optimize the three objectives, facilitate Pareto-front creation via multiple iterations, and establish evaluation functions grounded on the prescribed weight coefficients in order to attain the optimal solution.After comparison, it is found that after optimization, compared with the initial parameters, the torque ripple at the rated point can be reduced by 37.5%, the efficiency can be increased by 7.7%, and the effective material cost can be reduced by 16.8%. Finally, a 2 kW prototype is made for performance test. Compared with FEM, the experimental back EMF is 7.8% lower, and its THD is about 2.9% higher. At the rated current, the measured output torque is 42.2 Nm, which is 5.5% lower than FEM, and the efficiency is 87.3%, which is 3.4% lower than FEM.The above errors are mainly caused by the processing or assembly of parts and components, and the errors are within an acceptable range. The following conclusions can be drawn from the simulation analysis: (1) The structural parameters of the motor can be comprehensively and quickly optimized through the proposed optimization framework. Considering the limitations of maximum bus voltage and demagnetization of PMs, respectively, the effectiveness and rationality of the results are ensured. (2) Cluster analysis of operating points can reduce the time and cost of subsequent finite element calculations. (3) RSM is combined with the NSGA-Ⅱ algorithm optimization to quickly and accurately optimize parameters while decreasing the possibility of entering local optima.
樊英, 陈秋蒴, 陈俊磊, 雷宇通. 基于无人配送车辆运行工况的交替极游标轮毂电机优化设计[J]. 电工技术学报, 2023, 38(19): 5141-5151.
Fan Ying, Chen Qiushuo, Chen Junlei, Lei Yutong. Optimization Design of Consequent Pole Vernier Wheel Motor Based on Operating Conditions of Unmanned Delivery Vehicles. Transactions of China Electrotechnical Society, 2023, 38(19): 5141-5151.
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2023, Vol. 38 
