Open Access
 Issue JNWPU Volume 39, Number 6, December 2021 1289 - 1295 https://doi.org/10.1051/jnwpu/20213961289 21 March 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## 1 IHSDE算法原理

IHSDE算法同时具有HS算法与DE算法的优点, 以HS算法为主体, 在新和声的生成方式上加入了DE算法的思想, 在更新和声记忆库时引入精英保留策略有效改善了算法的性能。

### 1.3 精英保留策略

 图1IHSDE算法流程图

## 2 算法性能测试

Schwefel 2.26函数表达式

 图23种优化算法的函数测试情况

3种优化算法搜索能力比较

## 3 DBC-TFPM尺寸和性能分析

(6) 式给出了最大转矩密度公式, 具体推导过程可参考文献[13]。

 图3DBC-TFPM结构图
 图4铁心和永磁体尺寸平面图

## 4 DBC-TFPM参数优化

### 4.1 选择优化目标函数

DBC-TFPM应用于新能源汽车时, 作为动力核心部件需要提供足够的输出转矩, 由于车内空间紧凑, 还需尽可能减小电机体积。此外, 横向磁场电机特殊的结构设计导致其生产成本较高, 在保证性能的前提下, 应尽可能地降低成本。因此, 本文选择最大转矩密度和生产成本为优化子目标。为了消除数量级不同带来的优化误差, 设计优化目标函数为

### 4.6 优化分析

 图5优化目标函数相关变化曲线
 图6优化设计变量变化曲线

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3种优化算法搜索能力比较

## All Figures

 图1IHSDE算法流程图 In the text
 图23种优化算法的函数测试情况 In the text
 图3DBC-TFPM结构图 In the text
 图4铁心和永磁体尺寸平面图 In the text
 图5优化目标函数相关变化曲线 In the text
 图6优化设计变量变化曲线 In the text

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