• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

基于改进人工蜂群算法的轮毂电机多目标优化

张河山 邓兆祥 妥吉英 张羽 陶胜超

张河山, 邓兆祥, 妥吉英, 张羽, 陶胜超. 基于改进人工蜂群算法的轮毂电机多目标优化[J]. 西南交通大学学报, 2019, 54(4): 671-678. doi: 10.3969/j.issn.0258-2724.20170094
引用本文: 张河山, 邓兆祥, 妥吉英, 张羽, 陶胜超. 基于改进人工蜂群算法的轮毂电机多目标优化[J]. 西南交通大学学报, 2019, 54(4): 671-678. doi: 10.3969/j.issn.0258-2724.20170094
ZHANG Heshan, DENG Zhaoxiang, TUO Jiying, ZHANG Yu, TAO Shengchao. Multi-Objective Optimum Design for in-Wheel Motor Based on Improved Artificial Bee Colony Algorithm[J]. Journal of Southwest Jiaotong University, 2019, 54(4): 671-678. doi: 10.3969/j.issn.0258-2724.20170094
Citation: ZHANG Heshan, DENG Zhaoxiang, TUO Jiying, ZHANG Yu, TAO Shengchao. Multi-Objective Optimum Design for in-Wheel Motor Based on Improved Artificial Bee Colony Algorithm[J]. Journal of Southwest Jiaotong University, 2019, 54(4): 671-678. doi: 10.3969/j.issn.0258-2724.20170094

基于改进人工蜂群算法的轮毂电机多目标优化

doi: 10.3969/j.issn.0258-2724.20170094
基金项目: 国家高技术研究发展计划(863计划)资助项目(2012AA111803);重庆市科委攻关项目(CSTC,2010AA6039)
详细信息
    作者简介:

    张河山(1988—),男,博士研究生,研究方向为汽车系统动力学,E-mail:zhangheshan@qq.com

    通讯作者:

    邓兆祥(1962—),男,教授,博士生导师,研究方向为汽车振动噪声控制与汽车系统动力学,E-mail:zxdeng@cqu.edu.cn

Multi-Objective Optimum Design for in-Wheel Motor Based on Improved Artificial Bee Colony Algorithm

  • 摘要: 为了提高轮毂电机功率密度、降低其材料成本,提出一种改进的人工蜂群算法对轮毂电机性能进行优化设计. 首先利用磁路法建立外转子永磁式轮毂电机各项性能的表达式;其次通过引入个体极值、群体极值以及一对异步缩放因子来克服传统人工蜂群算法收敛速度较慢、探索与开发能力不平衡等缺点;以磁极对数、气隙长度、永磁体厚度等电磁参数为设计变量,将电机的有效质量、功率损耗和材料成本线性加权组成单目标函数,并采用障碍函数法将有约束的非线性目标函数转化为非约束的形式;最后利用遗传算法、传统人工蜂群算法和改进的人工蜂群算法对轮毂电机进行优化设计,并通过有限元法和样机实验验证了计算结果的正确性. 研究结果表明:相较于传统人工蜂群算法,改进的人工蜂群算法使目标函数收敛速度更快;相较于遗传算法和传统人工蜂群算法,改进后的算法使目标函数值最小;相较于原设计方案,优化后轮毂电机有效质量降低13.4%,材料成本降低34.4%,功率损耗降低44.2%,电机效率提高12.0%.

     

  • 图 1  轮毂电机总成

    Figure 1.  Diagram of the in-wheel motor assembly

    图 2  定子槽几何尺寸

    Figure 2.  Geometric dimensions of stator slots

    图 3  异步缩放因子变化趋势

    Figure 3.  Trend of asynchronous scaling factor

    图 4  改进人工蜂群算法主要流程

    Figure 4.  Flow chart of the improved ABC algorithm

    图 5  ABC和IABC的迭代过程对比

    Figure 5.  Comparison of the objective function ralues in iterative process between the ABC and IABC

    图 6  空载气隙磁密

    Figure 6.  Air gap flux density in no-load test

    图 7  轮毂电机测控平台

    Figure 7.  Test platform of in-wheel motor

    图 8  轮毂电机效率图

    Figure 8.  Efficiency of the in-wheel motor

    表  1  优化后的设计变量值

    Table  1.   Optimized design variable values

    算法 p δ/cm Q1 hm/cm L/cm Ns Di1/cm d11/mm
    初始方案 28 0.10 54 0.46 4.2 40 19.00 1.3
    GA 22 0.10 52 0.32 3.8 34 19.20 1.4
    ABC 20 0.12 50 0.30 3.5 34 19.37 1.5
    IABC 16 0.12 48 0.30 3.5 32 19.50 1.4
    下载: 导出CSV

    表  2  初始方案和各种算法优化结果

    Table  2.   Results of initial design and optimized design with different algorithms

    算法 电机有效
    质量/kg
    电机材料
    成本/元
    功率
    损耗/W
    效率/%
    初始方案 24.17 1 169.5 2 345.2 78.68
    GA 22.15 880.3 1 972.3 82.07
    ABC 21.42 826.4 1 537.8 86.02
    IABC 20.92 766.8 1 307.9 88.11
    下载: 导出CSV
  • 卢东斌,欧阳明高,谷靖,等. 电动汽车永磁同步电机最优制动能量回馈控制[J]. 中国电机工程学报,2013,33(3): 83-91.

    LU Dongbin, OUYANG Minggao, GU Jing, et al. Optimal regenerative braking control for permanent magnet synchronous motors in electric vehicles[J]. Proceedings of the CSEE, 2013, 33(3): 83-91.
    ROMERAL L, URRESTY J C. Modeling of surface-mounted permanent magnet synchronous motors with stator winding interturn faults[J]. IEEE Transactions on Industrial Electronics, 2011, 58(5): 1576-1585. doi: 10.1109/TIE.2010.2062480
    孙跃,谭若兮,唐春森,等. 一种应用于电动汽车的新型耦合机构优化设计[J]. 西南交通大学学报,2018,53(5): 1078-1086.

    SUN Yue, TAN Ruoxi, TANG Chunsen, et al. Optimized design of new coupling mechanism for electric vehicles[J]. Journal of Southwest Jiaotong University, 2018, 53(5): 1078-1086.
    RAHIDEH A, KORAKIANITIS T, RUIZ P, et al. Optimal brushless DC motor design using genetic algorithms[J]. Journal of Magnetism and Magnetic Materials, 2010, 322(22): 3680-3687. doi: 10.1016/j.jmmm.2010.07.025
    陈齐平,舒红宇,任凯,等. 基于改进遗传算法的微型电动车轮毂电机优化设计[J]. 中南大学学报(自然科学版),2012,43(8): 3013-3018.

    CHEN Qiping, SHU Hongyu, REN Kai, et al. Optimization design of driving in-wheel motor of micro-electric vehicle based on improvement genetic algorithm[J]. Journal of Central South University (Science and Technology), 2012, 43(8): 3013-3018.
    李立毅,唐勇斌,刘家曦,等. 多种群遗传算法在无铁心永磁直线同步电机优化设计中的应用[J]. 中国电机工程学报,2013,33(15): 69-77.

    LI Liyi, TANG Yongbin, LIU Jiaxi, et al. Application of the multiple population genetic algorithm in optimum design of air-core permanent magnet linear synchronous motors[J]. Proceedings of the CSEE, 2013, 33(15): 69-77.
    NGUYEN T D, LANFRANCHI V. Comparison of optimization algorithms for the design of a brushless DC machine with travel-time minimization[C]//2009 8th International Symposium on Advanced Electromechanical Motion Systems & Electric Drives Joint Symposium. Lille: IEEE, 2009: 1-6
    ANDERSEN S B, SANTOS I F. Evolution strategies and multi-objective optimization of permanent magnet motor[J]. Applied Soft Computing, 2012, 12(2): 778-792. doi: 10.1016/j.asoc.2011.10.013
    王晓远,高鹏. 基于进化策略的轮毂电机永磁体结构优化设计[J]. 中国电机工程学报,2015,35(4): 979-984.

    WANG Xiaoyuan, GAO Peng. Optimal design of permanent magnets of in-wheel motor based on evolution strategy[J]. Proceedings of the CSEE, 2015, 35(4): 979-984.
    LI S, YANG M. Particle swarm optimization combined with finite element method for design of ultrasonic motors[J]. Sensors and Actuators A: Physical, 2008, 148(1): 285-289. doi: 10.1016/j.sna.2008.08.004
    NAVARDI M J, BABAGHORBARI B, KETABI A. Efficiency improvement and torque ripple minimization of switched reluctance motor using FEM and seeker optimization algorithm[J]. Energy Conversion and Management, 2014, 78(2): 237-244.
    KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[J]. Journal of Global Optimization, 2007, 39(3): 459-471. doi: 10.1007/s10898-007-9149-x
    KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1): 108-132. doi: 10.1016/j.amc.2009.03.090
    KARABOGA D, OZTURK C. A novel clustering approach: artificial bee colony (ABC) algorithm[J]. Applied Soft Computing, 2011, 11(1): 652-657. doi: 10.1016/j.asoc.2009.12.025
    KARABOGA D, BASTURK B. On the performance of artificial bee colony (ABC) algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697. doi: 10.1016/j.asoc.2007.05.007
    毕晓君,王艳娇. 改进人工蜂群算法[J]. 哈尔滨工程大学学报,2012,33(1): 117-123. doi: 10.3969/j.issn.1007-7043.201102020

    BI Xiaojun, WANG Yanjiao. A modified artificial bee colony algorithm and its application[J]. Journal of Harbin Engineering University, 2012, 33(1): 117-123. doi: 10.3969/j.issn.1007-7043.201102020
    毕晓君,王艳娇. 加速收敛的人工蜂群算法[J]. 系统工程与电子技术,2011,33(12): 2755-2761. doi: 10.3969/j.issn.1001-506X.2011.12.34

    BI Xiaojun, WANG Yanjiao. Artificial bee colony algorithm with fast convergence[J]. Systems Engineering and Electronics, 2011, 33(12): 2755-2761. doi: 10.3969/j.issn.1001-506X.2011.12.34
    ZHU G, KWONG S. Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics and Computation, 2010, 217(7): 3166-3173. doi: 10.1016/j.amc.2010.08.049
    GAO W, LIU S, HUANG L. A global best artificial bee colony algorithm for global optimization[J]. Journal of Computational and Applied Mathematics, 2012, 236(11): 2741-2753. doi: 10.1016/j.cam.2012.01.013
    KARABOGA D, GORKEMLI B. A combinatorial artificial bee colony algorithm for traveling salesman problem[C]//2011 International Symposium on Innovations in Intelligent Systems and Applications. Turkey: IEEE, 2011: 50-53
    WANG X, TANG L. An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization[J]. Information Sciences, 2016, 348(2): 124-141.
    MARINI F, WALCZAK B. Particle swarm optimization (PSO). A tutorial[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 149(5): 153-165. doi: 10.1016/j.chemolab.2015.08.020
  • 加载中
图(8) / 表(2)
计量
  • 文章访问数:  643
  • HTML全文浏览量:  314
  • PDF下载量:  39
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-02-24
  • 修回日期:  2018-05-12
  • 网络出版日期:  2019-06-12
  • 刊出日期:  2019-08-01

目录

    /

    返回文章
    返回