动车组应急蓄电池箱的多目标优化

李娅娜; 高佳威

doi:10.13433/j.cnki.1003-8728.20220232

动车组应急蓄电池箱的多目标优化

doi: 10.13433/j.cnki.1003-8728.20220232

大连交通大学机车车辆工程学院, 辽宁大连 116028

基金项目:

国家自然科学基金项目 52075066

辽宁省教育厅项目 LJKZ0497

详细信息

作者简介:
李娅娜, 教授, 博士生导师, lyn1977522@163.com

中图分类号: TG156
计量
- 文章访问数: 58
- HTML全文浏览量: 25
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-06
- 刊出日期: 2024-01-25

Multi-objective Optimization of Emergency Battery Box for Bullet Train

School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China

摘要

摘要: 针对动车组的应急蓄电池箱的安全问题和轻量化设计要求，综合多个优化目标对其进行优化分析。将主要部件的厚度作为设计变量，以应急蓄电池箱的总质量和和恶劣工况下的应力最小为优化目标，以其第1阶固有频率为约束函数，使用Box-Behnken设计方法获取样本数据。利用样本数据建立低阶多项式响应面模型，结合第三代非支配排序遗传算法(NSGA-Ⅲ)进行多目标优化。结果表明: 相较于单一的响应面法或遗传算法，本文采用的响应面法与遗传算法结合的方式，使得优化后的参数更加合理，轻量化和安全性均得到了保障。
- 响应面模型 /
- NSGA-Ⅲ /
- Box-Behnken设计 /
- 轻量化 /
- 安全性
Abstract: Aiming at the safety problem and lightweight design requirements of train-set emergency battery box, the optimization analysis was carried out by integrating multiple optimization objectives. The thickness of the main components was taken as the design variables, the total mass of the emergency battery box and the minimum stress under harsh conditions were taken as the optimization objectives, and the first-order natural frequency was taken as the constraint function. The sample data were obtained by Box-Behnken design method. A low-order polynomial response surface model was established based on the sample data, and multi-objective optimization was carried out using the third-generation non-dominated sorting genetic algorithm (NSGA-Ⅲ). The results show that compared with the single response surface method or genetic algorithm, the combination of response surface method and genetic algorithm adopted in this paper makes the optimized parameters more reasonable, lightweight and safety are guaranteed.
- response surface model /
- NSGA-Ⅲ /
- Box-Behnken design /
- lightweight /
- security

HTML全文

图 1 基于响应面法与NSGA-Ⅲ的多目标优化流程

Figure 1. Multi-objective optimization procedures based on response surface method and NSGA-Ⅲ

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图 2 蓄电池箱的有限元模型

Figure 2. Finite element model of battery box

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图 3 板厚对各参数的灵敏度

Figure 3. The sensitivity of plate thickness to each parameter

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图 4 响应面模型

Figure 4. Response surface model

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图 5 Pareto图

Figure 5. Pareto diagram

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表 1 选取的9个板的厚度

Table 1. Thicknesses of selected nine plate

序号	编号	厚度/mm
1	X₁	2.0
2	X₂	2.0
3	X₃	3.0
4	X₄	4.0
5	X₅	4.0
6	X₆	5.6
7	X₇	5.0
8	X₈	7.0
9	X₉	7.0

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表 2 设计变量的初始值及上下限值

Table 2. Initial values and upper and lower limits of design variables

编号	下限值/mm	初始值/mm	上限值/mm
X₂	1.0	2.0	3.0
X₄	2.0	4.0	6.0
X₇	3.0	5.0	7.0
X₈	5.0	7.0	9.0
X₉	5.2	7.2	9.2

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表 3 Box-Behnken试验设计表

Table 3. Box-Behnken test design

参数	组1	组2	组3	组4	组5
X₂/mm	1.0	1.0	3.0	3.0	1
X₄/mm	2.0	6.0	2.0	6.0	4
X₇/mm	5.0	5.0	5.0	5.0	3
X₈/mm	7.0	7.0	7.0	7.0	7
X₉/mm	7.0	7.2	7.0	7.2	7.2
质量M/10³ kg	1.28	1.31	1.29	1.32	1.28
von Mises应力S/MPa	138	144	141	147	137
1阶固有频率/Hz	31.0	31.1	35.3	34.6	31.0

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表 4 原始值与优化后值的对比

Table 4. Comparison between the original value and the optimized value

参数名	初始值	优化后值	变化量/%
X₂/mm	2.00	1.00	-50
X₄/mm	4.00	2.00	-50
X₇/mm	5.00	3.00	-40
X₈/mm	7.00	5.00	-29
X₉/mm	7.20	6.32	-12.2
M/10³ kg	0.61	0.58	-5.0
S/MPa	142.8	130.4	-8.7
f₁/Hz	31.4	30.8	-1.9

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参考文献(15)

[1]	AZEEZ T M, MUDASHIRU L O, ASAFA T B, et al. Mechanical properties of Al 6063 processed with equal channel angular extrusion under varying process parameters[J]. International Journal of Engineering Research in Africa, 2021, 54: 23-32. doi: 10.4028/www.scientific.net/JERA.54.23
[2]	PANDA J N, ORQUERA E Y, WONG B C, et al. Prediction of optimal process parameters in tribocorrosion inhibition of steel pipes using response surface methodology[J]. Tribology Letters, 2021, 69(2): 73. doi: 10.1007/s11249-021-01441-x
[3]	崔宝珍, 孔维娜, 马恺. 多项式响应面代理模型在立柱结构优化中的应用[J]. 机械设计, 2017, 34(4): 44-48. CUI B Z, KONG W N, MA K, et al. Application of polynomial response surface surrogate model in the large column structure optimization[J]. Journal of Machine Design, 2017, 34(4): 44-48. (in Chinese)
[4]	鲁宜文, 王东方, 缪小冬, 等. 响应面和多目标遗传算法结合的副车架优化[J]. 机械设计与制造, 2018(4): 1-4. LU Y W, WANG D F, MIAO X D, et al. Optimization for sub-frame combining response surface with muti-objective genetic algorithm[J]. Machinery Design & Manufacture, 2018(4): 1-4. (in Chinese)
[5]	嵇友迪, 龚红英, 贾星鹏, 等. 基于响应面与遗传算法的汽车油箱托盘仿真优化[J]. 塑性工程学报, 2021, 28(12): 29-35. doi: 10.3969/j.issn.1007-2012.2021.12.004 JI Y D, GONG H Y, JIA X P, et al. Simulation and optimization of automobile fuel tank tray based on response surface and genetic algorithm[J]. Journal of Plasticity Engineering, 2021, 28(12): 29-35. (in Chinese) doi: 10.3969/j.issn.1007-2012.2021.12.004
[6]	孙喜龙, 王登峰, 荣宝军, 等. 响应面法在汽车侧面结构多目标优化中的应用[J]. 机械科学与技术, 2022, 41(7): 1039-1047. doi: 10.13433/j.cnki.1003-8728.20200448 SUN X L, WANG D F, RONG B J, et al. Applying response surface method to multi-objective optimization of automobile's side structure[J]. Mechanical Science and Technology for Aerospace Engineering for Aerospace Engineering, 2022, 41(7): 1039-1047. (in Chinese) doi: 10.13433/j.cnki.1003-8728.20200448
[7]	李永华, 李东明, 宫琦, 等. 动车组电机吊架多目标可靠性优化设计[J]. 机械科学与技术, 2021, 40(7): 1100-1105. doi: 10.13433/j.cnki.1003-8728.20200185 LI Y H, LI D M, GONG Q, et al. Multi-objective reliability optimization design of motor hanger for EMU[J]. Mechanical Science and Technology for Aerospace Engineering for Aerospace Engineering, 2021, 40(7): 1100-1105. (in Chinese) doi: 10.13433/j.cnki.1003-8728.20200185
[8]	MORALA P, CIFUENTES J A, LILLO R E, et al. Towards a mathematical framework to inform neural network modelling via polynomial regression[J]. Neural Networks, 2021, 142: 57-72. doi: 10.1016/j.neunet.2021.04.036
[9]	窦毅芳, 刘飞, 张为华. 基于改进交互验证策略的序贯响应面建模方法[J]. 机械强度, 2008, 30(5): 753-757. DOU Y F, LIU F, ZHANG W H. Improved cross validation strategy based sequential response surface method[J]. Journal of Mechanical Strength, 2008, 30(5): 753-757. (in Chinese)
[10]	李国良, 周煊赫, 孙佶, 等. 基于机器学习的数据库技术综述[J]. 计算机学报, 2020, 43(11): 2019-2049. LI G L, ZHOU X H, SUN J, et al. A survey of machine learning based database techniques[J]. Chinese Journal of Computers, 2020, 43(11): 2019-2049. (in Chinese)
[11]	安友军, 陈晓慧. 近似支配的NSGA-Ⅲ算法求解柔性作业车间调度问题[J]. 系统工程学报, 2021, 36(3): 416-432. AN Y J, CHEN X H. Approximate dominance NSGA-Ⅲ algorithm for solving flexible job-shop scheduling problem[J]. Journal of Systems Engineering, 2021, 36(3): 416-432. (in Chinese)
[12]	杜子学, 李云川, 文孝霞, 等. 基于SIMPACK/ISIGHT的跨座式单轨列车走行轮偏磨研究与优化[J]. 铁道车辆, 2016, 54(4): 1-5. DU Z X, LI Y C, WEN X X, et al. Research on side wear of the running wheel for straddle type monorail train based on SIMPACK/ISIGHT and the optimization[J]. Rolling Stock, 2016, 54(4): 1-5. (in Chinese)
[13]	王爱彬, 罗仁, 奚佳欣. 地铁车辆车体侧摆试验及参数灵敏度分析[J]. 城市轨道交通研究, 2021, 24(2): 60-63. WANG A B, LUO R, XI J X. Metro vehicle body sway test and parameters sensitivity analysis[J]. Urban Mass Transit, 2021, 24(2): 60-63. (in Chinese)
[14]	孙远韬, 陈凯歌, 章增增, 等. 基于近似模型的板料成形稳健优化方法研究[J]. 中国工程机械学报, 2021, 19(4): 283-288. SUN Y T, CHEN K G, ZHANG Z Z, et al. Research on robust optimization method of sheet metal forming based on approximate model[J]. Chinese Journal of Construction Machinery, 2021, 19(4): 283-288. (in Chinese)
[15]	徐晓岭, 王磊. 统计学[M]. 北京: 人民邮电出版社, 2015. XU X L, WANG L. Statistics[M]. Beijing: Posts & Telecom Press, 2015. (in Chinese)