Parameters Optimization for Hybrid Energy Storage System of Electric Vehicles Based on Cross-Entropy Algorithm
-
摘要: 为了提升电动汽车动力性能、降低车辆成本，以复合电源成本和车辆电耗最小为目标，通过交叉熵（cross- entropy，CE）算法对车载复合电源的参数优化进行了研究. 首先，以某款纯电动汽车为研究对象，根据能量与功率性能指标确定锂离子电池和超级电容的容量范围；其次，选取复合电源成本和车辆电耗建立多目标优化函数，并在ADVISOR环境中搭建车辆仿真模型；接着，采用CE算法，通过种群的不断迭代，更新高斯概率密度函数的均值和方差，找到复合电源参数的Pareto最优解集；最后，从最优Pareto解集中选取典型的匹配参数，分析复合电源成本、车辆电耗和整车性能. 研究结果表明：在满足基本约束的前提下，得到了由100个解组成的Pareto最优解集. 与第二代非劣排序遗传算法（non-dominated sorting genetic algorithm-Ⅱ，NSGA-Ⅱ）比较，CE算法有更好的收敛性与分布性；复合电源成本平均降低了9.49%，车辆电耗平均降低了22.81%； 此外，城市道路循环工况（urban dynamometer driving schedule，UDDS）下车速误差最大值降低16.15%，整车动力性也有显著提升，百公里加速时间缩短7.81%，最高车速提升1.98%.Abstract: In order to improve the dynamic performance of electric vehicles and reduce costs, a parameter optimization method for vehicle-mounted hybrid power supply based on cross-entropy (CE) algorithm is explored with the intent of minimizing the hybrid power supply cost and power consumption. Firstly, a hybrid electric vehicle is used as the object, and the capacity ranges of its lithium-ion batteries and super-capacitors are determined according to the energy and power performance indexes. Secondly, the multi-objective optimization function of minimizing power supply cost and power consumption and the vehicle simulation model are established in ADVISOR. Subsequently, with CE algorithm, the mean and variance of the Gaussian probability density function are updated by the continuous iterations of populations to find out the optimal Pareto solution set. Finally, the typical solutions are selected to analyze the cost, power consumption and vehicle performance. The results show that under the basic requirements, 100 optimal solutions are found, which constitute an optimal Pareto solution set. Compared with the results of (non-dominated sorting genetic algorithm-Ⅱ) NSGA-Ⅱ, the convergence and distribution of CE algorithm are better, the cost of hybrid power supply is reduced by 9.49% and the vehicle power consumption by 22.81% on average. Furthermore, the maximum error of vehicle speed is reduced by 16.15% under UDDS cycle condition, and the vehicle dynamic performance is improved significantly with the acceleration time of 100 km reduced by 7.81% and the maximum speed increased by 1.98%.
-
图 5 不同算法的锂离子电池SOC、温度及超级电容SOC
Figure 5. Lithium-ion battery SOC, supercapacitor SOC and temperature curve of different algorithms
表 1 整车参数
Table 1. Vehicle parameters
部件 参数 数值 整车 总质量/kg 1 191 长，宽，高/mm 4 410，1 750，1 000 轴距/mm 2 600 电机 最大功率/kW 75 电压等级/V 320 最大转速/（r•min−1） 10 000 最大转矩/（N•m） 200 锂离子电池 单体电池额定电压/V 3.2 单体电池额定容量/（A•h） 10～60 能量密度/（W•h•kg−1） 120 超级电容 额定电压/V 2.5 额定容量/F 1 000～3 500 能量密度/（W•h•kg−1） 6 
下载: 导出CSV
表 2 整车循环工况功率需求
Table 2. Power requirements in driving cycle
项目 正（驱动）需求 负（制动）需求 总能量需求/kJ 6 799 −440.2 循环中所占时间/h 21.50 2.683 平均功率需求/kW 5.270 −2.734 峰值功率需求/kW 39.70 −8.942
下载: 导出CSV
表 3 不同算法优化结果比较
Table 3. Comparison of different algorithm optimization results
项目 CE NSGA-II 优化前 最小车辆电耗/（kW•h） 2.241 8 2.125 1 7.140 1 平均车辆电耗/（kW•h） 3.254 0 3.562 8 7.140 1 最低电源成本/万元 8.311 1 9.432 8 16.080 平均电源成本/万元 10.937 1 14.169 2 16.080 锂离子电池DOD 0.061 0 0.086 0 0.154 0 超级电容DOD 0.426 1 0.465 2 制动能量回收/kJ −6 198.6 −5 469.1 −5 331.2 放电效率 0.995 2 0.989 5 0.922 0
下载: 导出CSV
表 4 整车动力性能比较
Table 4. Comparison of vehicle dynamic performance
项目 CE 算法优化 NSGA-II 算法优化 未优化 最高车速/（km•h−1） 157.55 （提高 14.64%） 154.49 （提高 12.41%） 137.43 15 km/h 最大爬坡度/% 35（提高 25.00%） 33（提高 17.85%） 28 5 s 能达到的最远距离/m 195.8 （提高 3.87%） 188.9 （提高 0.21%） 188.5 0～50 km/h 加速时间/s 5.0 （提高 5.66%） 5.2 （提高 1.88%） 5.3 50～80 km/h 加速时间/s 5.8 （提高 48.67%） 6.0 （提高 46.9%） 11.3 0～100 km/h 加速时间/s 17.7 （提高 11.94%） 19.2 （提高 4.47%） 20.1
下载: 导出CSV
-
马建,刘晓东,陈轶嵩,等. 中国新能源汽车产业与技术发展现状及对策[J]. 中国公路学报,2018,31(8): 1-19. doi: 10.3969/j.issn.1001-7372.2018.08.001MA Jian, LIU Xiaodong, CHEN Yisong, et al. Current status and countermeasures for China ’s new energy automobile industry and technology development[J]. China Journal of Highway and Transport, 2018, 31(8): 1-19. doi: 10.3969/j.issn.1001-7372.2018.08.001 IANNUZZI D, CICCARELLI F, LAURIA D. Stationary ultracapacitors storage device for improving energy saving and voltage profile of light transportation networks[J]. Transportation Research Part C, 2011, 21(1): 321-337. AHMADI S, BATHAEE S M T. Multi-objective genetic optimization of the fuel cell hybrid vehicle supervisory system:fuzzy logic and operating mode control strategies[J]. International Journal of Hydrogen Energy, 2015, 40(36): 12512-12521. doi: 10.1016/j.ijhydene.2015.06.160 GAO C, ZHAO J, WU J, et al. Optimal fuzzy logic based energy management strategy of battery/ supercapacitor hybrid energy storage system for electric vehicles[C]//Intelligent Control and Automation. [S.l.]: IEEE, 2016: 98-102. 杨阳,崔维隆,苏岭,等. 一种新型混合动力传动系统匹配设计与性能仿真[J]. 中国公路学报,2014,27(9): 111-118. doi: 10.3969/j.issn.1001-7372.2014.09.015YANG Yang, CUI Weilong, SU Ling, et al. Matching design and performance simulation of a new hybrid powertrain[J]. China Journal of Highway and Transport, 2014, 27(9): 111-118. doi: 10.3969/j.issn.1001-7372.2014.09.015 HU X, MURGOVSKI N, JOHANNESSON L M, et al. Comparison of three electrochemical energy buffers applied to a hybrid bus powertrain with simultaneous optimal sizing and energy management[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(3): 1193-1205. doi: 10.1109/TITS.2013.2294675 胡建军,郑勇,胡志华,等. 纯电动汽车复合储能系统参数匹配及控制策略[J]. 中国公路学报,2018,31(3): 142-150. doi: 10.3969/j.issn.1001-7372.2018.03.016HU Jianjun, ZHNEG Yong, HU Zhihua, et al. Parameter matching and control strategy of hybrid energy storage system for pure electric vehicle[J]. China Journal of Highway and Transport, 2018, 31(3): 142-150. doi: 10.3969/j.issn.1001-7372.2018.03.016 于远彬,王庆年,王加雪,等. 混合动力汽车车载复合电源参数匹配及其优化[J]. 吉林大学学报(工学版),2008,38(4): 764-768.YU Yuanbin, WANG Qingnian, WANG Jiaxue, et al. Parameter matching and optimization of hybrid vehicle power supply for hybrid electric vehicle[J]. Journal of Jilin University (Engineering Edition), 2008, 38(4): 764-768. 王伟达,王言子,项昌乐,等. 混合动力车制动工况分析与储能装置参数匹配[J]. 哈尔滨工业大学学报,2014,46(9): 74-79. doi: 10.11918/hitxb20140913WANG Weida, WANG Yanzi, XIANG Changle, et al. Analysis of brake condition and parameter matching of hybrid energy storage system for hybrid electric vehicles[J]. Journal of Harbin Institute of Technology, 2014, 46(9): 74-79. doi: 10.11918/hitxb20140913 朱曰莹,赵桂范,杨娜,等. 电动汽车动力系统参数匹配及优化[J]. 哈尔滨工业大学学报,2013,45(7): 90-95. doi: 10.11918/j.issn.0367-6234.2013.07.017ZHU Yueying, ZHAO Guifan, YANG Na, et al. Parameters match and optimization for the drive system of electric vehicle[J]. Journal of Harbin Institute of Technology, 2013, 45(7): 90-95. doi: 10.11918/j.issn.0367-6234.2013.07.017 WANG Q, FRANK A A. Plug-in HEV with CVT:configuration,control,and its concurrent multi-objective optimization by evolutionary algorithm[J]. International Journal of Automotive Technology, 2014, 15(1): 103-115. doi: 10.1007/s12239-014-0012-z 王庆年, 王光平, 王鹏宇, 等. 基于成本优化的插电式混合动力参数匹配[J]. 吉林大学学报（工学版）, 2016(2): 340-347.WANG Qingnian, WANG Guangping, WANG Pengyu, et al. Parameter matching for plug-in hybrid electric vehicle based on cost optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2016(2): 340-347. WU X, CAO B, LI X, et al. Component sizing optimization of plug-in hybrid electric vehicles[J]. Applied Energy, 2011, 88(3): 799-804. doi: 10.1016/j.apenergy.2010.08.018 邓涛,林椿松,李亚南,等. 采用NSGA-Ⅱ算法的混合动力能量管理控制多目标优化方法[J]. 西安交通大学学报,2015,49(10): 143-150. doi: 10.7652/xjtuxb201510023DENG Tao, LING Chunsong, LI Yanan, et al. A multi-objective optimization method for energy management control of hybrid electric vehicles using NSGA-II algorithm[J]. Journal of Xi’an Jiaotong University, 2015, 49(10): 143-150. doi: 10.7652/xjtuxb201510023 周放,宋传学,梁天唯,等. 采用NSGA-Ⅱ算法的车载复合电源参数匹配[J]. 吉林大学学报(工学版),2017,47(5): 1336-1343.ZHOU Fang, SONG Chuanxue, LIANG Tianwei, et al. Parameter matching of on-board hybrid energy storage system using NSGA-Ⅱ algorithm[J]. Journal of Jilin University (Engineering Edition), 2017, 47(5): 1336-1343. 宋传学,周放,肖峰,等. 基于凸优化的车载复合电源参数匹配[J]. 机械工程学报,2017,53(16): 44-51. doi: 10.3901/JME.2017.16.044SONG Chuanxue, ZHOU Fang, XIAO Feng, et al. Parameter matching of on-board hybrid energy storage system based on convex optimization method[J]. Journal of Mechanical Engineering, 2017, 53(16): 44-51. doi: 10.3901/JME.2017.16.044 田文奇, 和敬涵, 姜久春, 等. 基于自适应变异粒子群算法的电动汽车换电池站充电调度多目标优化[J]. 电网技术, 2012(11): 25-29.TIAN Wenqi, HE Jinghan, JIANG Jiuchun, et al. Multi-objective optimization of charging dispatching for electric vehicle battery swapping station based on adaptive mutation particle swarm optimization[J]. Power System Technology, 2012(11): 25-29. DE BOER P T, KROESE D, MANNOR S, et al. A tutorial on the cross-entropy method[J]. Annals of Operations Research, 2005, 134(1): 19-67. doi: 10.1007/s10479-005-5724-z 袁义悦. 基于ADVISOR二次开发的车载复合电源参数匹配研究[D]. 哈尔滨: 哈尔滨工业大学, 2012. MARANO V, ONORI S, GUEZENNEC Y, et al. Lithium-ion batteries life estimation for plug-in hybrid electric vehicles[C]//Vehicle Power and Propulsion Conference, 2009. [S.l.]: IEEE, 2009: 536-54. DUO Z, CHENXI H, QICHAO T. CEGA: research on improved multi-objective CE optimization algorithm[C]//37th Chinese Control Conference. [S.l.]: IEEE, 2018: 2463-2467. -
下载:
点击查看大图
图(5) / 表(4)
计量
- 文章访问数: 710
- HTML全文浏览量: 339
- PDF下载量: 15
- 被引次数: 0