混合动力汽车参数的交叉—变异蜂群算法优化

doi:10.3969/j.issn.1674-8484.2021.02.006

摘要/Abstract

摘要：

为了减少混合动力汽车（HEV）油耗和有害气体排放量,提出了基于交叉—变异蜂群算法的参数优化方法,通过建立车辆的动力学模型、关键部件模型,综合运用发动机、电机及动力电池的效率,制定了能量管理策略。以动力系统参数和能量控制策略参数为优化变量,建立了多目标优化模型。在蜂群算法基础上,使用交叉—变异策略加深局部搜索方法的优化深度,使用半随机半保留方法提高了全局搜索效率。结果表明：经美国城市循环工况（UDDS）工况验证,优化后油耗减少了4.85%,CO排放量降低了19.83%,碳氢化合物（CH）排放量降低了8.08%,氮氧化物（NO_x）排放量降低了7.08%。这说明了交叉—变异蜂群算法在车辆参数优化中的有效性。

关键词: 混合动力汽车(HEV), 能量管理策略, 有害气体排放, 参数优化, 交叉—变异策略, 蜂群算法

Abstract:

A parameters optimization method was proposed based on crossover-mutation bee colony algorithm to reduce fuel consumption and harmful gases emissions of hybrid electric vehicle (HEV). Energy management strategy was formulated by establishing vehicle dynamic model and key component model, comprehensively utilizing the efficiency of engine, motor and power battery. Taking the parameters of power system and energy control strategy as optimization variables, a multi-objective optimization model was established. On the basis of bee colony algorithm, crossover and mutation strategy were used to deepen local searching method, and half stochastic and half reserved method was used to improve global searching efficiency. The results show that verified by the urban dynamometer driving schedule (UDDS) condition, fuel consumption after optimization decreases by 4.85%, CO emission by 19.83%, hydrocarbon compound (CH) by 8.08%, nitrogen oxides (NO_x) compound by 7.08%. This indicates the validity of crossover-mutation bee colony reckoning on parameters optimization.

Key words: hybrid electric vehicle (HEV), energy management strategy is ruled, harmful gases emissions, parameters optimization, crossover-mutation strategy, bee colony algorithm

中图分类号:

U461.8

刘建辉, 姚方方, 张彦. 混合动力汽车参数的交叉—变异蜂群算法优化[J]. 汽车安全与节能学报, 2021, 12(2): 186-192.

LIU Jianhui, YAO fangfang, ZHANG Yan. Parameters optimization of hybrid electric vehicle based on crossover-mutation bee colony algorithm[J]. Journal of Automotive Safety and Energy, 2021, 12(2): 186-192.

图/表 11

参考文献 10

[1]	吴江, 吴强, 周一丹, 等. 混合动力变速箱齿轮啸叫分析与优化验证[J]. 机械设计与制造, 2019(12):97-100.
	WU Jiang, WU Qiang, ZHOU Yidan, et al. Analysis and optimization of a hybrid transmission gear whine noise[J]. Mach Des Manu, 2019(12):97-100. (in Chinese)
[2]	刘永刚, 李杰, 秦大同, 等. 基于多工况优化算法的混合电动汽车参数优化[J]. 机械工程学报, 2017,53(16):61-69.
	LIU Yonggang, LI Jie, QIN Datong, et al. Parameter optimization of hybrid electric vehicle based on multi-cycle optimization algorithm[J]. J Mech Engi, 2017,53(16):61-69. (in Chinese)
[3]	张怡然, 赵韩, 黄康, 等. 混合动力汽车多目标参数解耦优化方法研究[J]. 机械传动, 2019,43(6):6-12.
	ZHANG Yiran, ZHAO Han, HUANG Kang, et al. Research of multi-objective parameter decoupled optimization method for hybrid electric vehicle[J]. J Mech Transmission, 2019,43(6):6-12. (in Chinese)
[4]	Sim K, Oh S M, Namkoong C, et al. Control strategy for clutch engagement during mode change of plug-in hybrid electric vehicle[J]. Int’l J Automotive Tech, 2017,18(5):901-909.
[5]	WANG Xiangyu, LIANG Li, JIAN Song. Hierarchical control of dry clutch for engine-start process in a parallel hybrid electric vehicle[J]. IEEE Trans Transp Electrification, 2016,2(2):231-243. doi: 10.1109/TTE.2016.2535316 URL
[6]	孙冬野, 陈元, 范曾雁. 搭载回流式动力传动系统的PHEV参数优化[J]. 重庆大学学报, 2019,42(6):1-12.
	SUN Dongye, CHEN Yuan, FAN Zengyan. Optimization of PHEV parameters with reflux power coupling transmission system[J]. J Chongqing Univ, 2019,42(6):1-12. (in Chinese)
[7]	刘辉, 李训明, 王伟达, 等. 基于最优功率分配因子的插电式混合动力汽车实时能量管理策略研究[J]. 机械工程学报, 2019,55(4):91-101.
	LIU Hui, LI Xunming, WANG Weida. et al. Optimal power allocation factor based real time energy management strategy for a plug-in hybrid electric vehicle[J]. J Mech Engi, 2019,55(4):91-101. (in Chinese)
[8]	谭燕妮. 混合动力汽车牵引电机转矩单独控制策略分析[J]. 机械设计与制造, 2016(9):93-97.
	TAN Yanni. Single control strategy analysis on hybrid electric vehicle traction motor torque[J]. Mach Des Manu, 2016(9):93-97. (in Chinese)
[9]	赵旭芳, 梁昔明. 改进的人工蜂群算法及其在参数优化中的应用[J]. 计算机仿真, 2019,36(9):320-325.
	ZHAO Xufang, LIANG Ximing. Improved artificial bee colony algorithm and its application in parameter optimization[J]. Computer Simulation, 2019,36(9):320-325. (in Chinese)
[10]	袁方. 测试用例优先排序技术优化研究[D]. 北京: 北京化工大学, 2019.
	YUAN Fang. Optimization for test case prioritization[D]. Beijing: Beijing university of chemical technology, 2019. (in Chinese)

类型	参数	取值范围
传动系统	1档位速比,i_g1	[12.9,14]
	2档位速比,i_g2	[7,8.1]
	3档位速比,i_g3	[4.5,5.5]
	4档位速比,i_g4	[3.3,4.3]
	5档位速比,i_g5	[2.3,3.3]
	主减速器速比,i₀	[0.5,2]
	电池数量,N_b	[20,30]
	发动机最大功率,P_emax/kW	[35,70]
控制策略	速度阈值,v_low/ （km·h^-1）	[27,33]
	相对电量阈值,S_th	[0.25,0.35]
	目标电量,S₀	[0.5,0.6]

类型	参数	取值范围
传动系统	1档位速比,i_g1	[12.9,14]
	2档位速比,i_g2	[7,8.1]
	3档位速比,i_g3	[4.5,5.5]
	4档位速比,i_g4	[3.3,4.3]
	5档位速比,i_g5	[2.3,3.3]
	主减速器速比,i₀	[0.5,2]
	电池数量,N_b	[20,30]
	发动机最大功率,P_emax/kW	[35,70]
控制策略	速度阈值,v_low/ （km·h^-1）	[27,33]
	相对电量阈值,S_th	[0.25,0.35]
	目标电量,S₀	[0.5,0.6]

0~100 km/h加速时间	≤12 s
40~100 km/h加速时间	≤8 s
以20 km/h持续爬坡10 s的坡度	≤30%
速度跟踪误差	≤0.2 km/h
最高车速	≥140 km/h

0~100 km/h加速时间	≤12 s
40~100 km/h加速时间	≤8 s
以20 km/h持续爬坡10 s的坡度	≤30%
速度跟踪误差	≤0.2 km/h
最高车速	≥140 km/h

整车质量	1.350 t
风阻因数	0.335
发动机最大功率	41 kW
电机最大功率	75 kW
电机最大转速	1万 r / min
单个电池指标	12 V,25 Ah
主减速器减速比	1
迎风面积	2 m²
发动机排量	1 L
发动机最大输出转矩	81 Nm
电机最大输出转矩	273 Nm
蓄电池数量	25
变速器5档变速比	13.35、7.57、5.01、3.77、2.84